# BM(book-to-market ratio) factor: medium-term momentum and long-term reversal

- Liu Wei-qi
^{1}and - Zhang Jingxing
^{1}Email author

**Received: **10 May 2017

**Accepted: **28 December 2017

**Published: **24 January 2018

## Abstract

To explain medium-term momentum and long-term reversal, we use the difference between the optional model and the CAPM model to construct a winner-loser portfolio. According to the CAPM model’s zero explanatory ability with respect to stock market anomalies, we obtain an anomaly interpretative model. This study shows that this anomaly interpretative model can explain stock market perceptions and medium-term momentum. Most importantly, BM is a critical factor in the model’s explanatory ability. We present a robustness test, which includes selecting new sample data, adding new auxiliary variables, changing sample years, and adding industry fixed effects. In general, the BM effect does have considerable explanatory power in medium-term momentum and long-term reversal.

## Keywords

## Introduction

In a completely effective market, stock market volatility is random. However, through daily observations, we find that the stock market often shows regular fluctuations, which is counter intuitive. These cyclical fluctuations often occur during a certain period and are known as stock market visions. These phenomena have attracted academic attention because they can no longer be interpreted by traditional methods. The momentum and reversal effect, as two typical stock market visions, have caused heated discussion in the academic community. Retail investors and institutional investors believe that grasping momentum or reversal means that a stock's future returns can be predicted for profit. Some such cases do exist. Jegadeesh and Titman (1993) first discovered and proposed that the US securities market momentum effect exists for three to 12 months and provided a short-term profitability strategy. The authors also found that this strategy can obtain 1% of the excess return rate within a certain period. Wang Yonghong and Zhao Xuejun (2001) used one month for the smallest sort period and discovered that China's stock market has a clear reversal effect. The authors also proved that the inertial effect of China's stock market is not obvious. However, scholars have not given a definitive answer as to how to grasp these visions. Research is still advancing incrementally. This paper focuses on the interpretation of the above two visions, constructs a model, and identifies a new visions model that can be applied to the explanation of the two anomalies at the same time. First, we explain several important basic concepts and previous research results.

Momentum effect is also known as the inertial effect. It implies that the stock had a higher return in the past and will have a higher return in the future; lower stock yields in the past mean that the future return will be lower. Jegadeesh and Titman (1993) were the first academics to discover that the momentum effect existed in the US securities market in three to 12 months. Soon afterward, they offered a short-term profitability strategy and found that the strategy, within a certain period, obtained 1% excess return. Then, Kaul and Conrad (1993) used shares of the stock listed on the New York Stock Exchange and the US Stock Exchange during the period 1926 to 1989 to study the stock returns during eight different investment periods. The research found that four of the investment strategies achieved significant profits, two strategies belong to the momentum strategy, the other two are reverse strategy. Almost at the same time, Rouwenhorst analyzed the momentum effects of 12 European countries' stock markets and found that almost all of them showed short-term momentum effect and to a greater extent than the US market. While Rouwenhorst confirmed that applying momentum strategies to emerging markets could acquire significant profit, some scholars discovered that investors tended to adopt momentum strategy in their decision making. For example, Chen found that US investors have momentum tendency when they make medium-term investment decisions. Given this, we find that the stock market and the investors are affected by momentum effect within three to 12 months, which indicates the existence of the medium-term momentum effect. This effect brings tangible benefits for investors. Griffin (2003) used 40 countries to explain the momentum effect of macroeconomic cycle risk. He found that momentum gains were present and significant in these countries, and regardless of whether the economic cycle was in an upswing or a declining phase, the momentum gains were significantly positive. Thus, the momentum effect and the macroeconomic cycle do not have a significant relationship. Lee and Swaminathan (2000) first studied the relationship between the momentum effect and stock trading volume from the perspective of trading volume.

The author concluded that the trading volume of the stock can predict the income of the momentum strategy and the duration of the momentum effect. When using momentum strategies in high stock returns, the effect of the momentum effect will become less and will continue for a shorter time. In addition to the long-term reversal effects found in the US market, scholars found a reversal effect in other countries. This finding indicates that the effect is not the reason for data mining. Chan et al. (1996) and Chui et al. (2000) found short-term reversal effects in the Japanese market. Baytas and Cakici (1999) note long-term reversal effects in the other seven countries.

After understanding the mid-momentum effect, we find that the reversal effect becomes easier to interpret. In simple terms, the reversal effect is that the stock that performed poorly in the past period will show a better result in the future. De bondt and Thaler (1985) found that the portfolio of the worst performing 10% stocks paid 10% over the stocks that were winners after the three-year formation; the loser portfolio showed a higher than average market return at 19.6% while the winner portfolio was still lower than 5%. This study confirmed that the reversal effect exists over a long period, and reversal strategies can be used to achieve substantial returns over a longer period.

Much controversy surrounds the existence of the momentum effect and the reversal effect on developed countries’ stock markets. Zhu (2003) is one of the representative scholars and has proved that China does not have a reversal effect through the study of the securities market. Chen Qiao and Wang Shi (2003) found that the inertial strategy based on the industry portfolio showed significant excess returns. Hameed and Ting (2000) found a significant price reversal effect in the Malaysian stock market indicating that there is a reversal effect in developed country stock markets. Gaunt’s (2000) study found that the Australian market also shows a significant reversal effect. His sample interval was from 1974 to 1997, the formation period was five years and, after an 18-month holding period, the reversal effect appeared. From the theoretical results of these scholars, we conclude that there are divergent views on whether there are momentum and reversal trends in the Chinese market, and the conclusions are totally different. The focus of our study is to clarify medium-term momentum and long-term reversal effects rather than prove their existence. Based on the above literature, we know that the reversal effect is a ubiquitous phenomenon and is concentrated in the medium and long term. Thus, our decision will be more inclined to choose the mature US financial market so that the statement on the existence of medium-term momentum and long-term reversal will be unified. The US stock market has become a better option for our empirical analysis.

Typically, scholars are divided into two categories: behavioral finance and traditional finance. Behavioral finance scientists often use the theory of overreaction and under-reaction to explain the reversal and momentum effects. The view of long-term overreaction was first introduced by De Bond and Thaler (1985, 1987). Additionally, there are several interpretative models in behavioral finance. The BSV model (Barberis, Shleifer, & Vishny, 1998) assumes that market investors have two deviations when they make personal decisions. One is a representative bias; because investors so easily overlook recent data, the result is over-reaction. The other is conservative bias; investors are insensitive to new information causing inadequate response to the information. The DHS model (Daniel, Hirshleifer, & Subramanyam, 1998) divides investors into information-based investors and non-information investors and considers whether investors are sensitive to new information. The HS model (Hong & Stein, 1999), based on assumptions concerning investors’ sensitivity and type, interprets the under-reaction from different perspectives. Fama and French (1996), who are representatives of traditional finance schools, analyzed the reversal and momentum of stock returns and confirmed that the benefits of long-term reversal strategies can be explained by their three-factor model, but the model cannot explain the medium-term momentum effect. Fama and French (1996) also believed that the CAPM vision is due to the CAPM model, which lacks the consideration of other necessary risk factors. Considering the problem with the CAPM model, our analysis is established based on the traditional financial school, which uses a model similar to the factor model to explain the two visions.

As mentioned, the BETA value in the CAPM model is not a reliable risk indicator. Some scholars found that if the new risk factor is added into the factor models, some excess returns can be explained. Some type of vision will disappear. But what variable should be added to the factor model to explain the medium-term momentum and long-term reversal? Scholars failed to reach a consensus on this point. Following the traditional financial idea, we consider the type of impact that occurs from adding BM and ROE into the model.

First, we validate the zero interpretation ability about the medium-term momentum and long-term reversal of the CAPM model. Then, we use the benefit forecast model obtained through the MC model (Lyle & Wang, 2015) to construct independent variables difference and obtain the vision interpretation model. This paper chooses the MC model as the basic model. We need to prove the following: First, this model has better ability than the CAPM model to forecast future stock earnings. Second, this model has the same ability as the three-factor model and the four factors in returning a prediction. The empirical test found that medium-term momentum was largely explained, and a small part of the long-term reversal was explained.

Additionally, through a series of studies, we discovered the BM as the most critical factor in explaining ability. Its value is totally different under the efficient market hypothesis. The long-term reversal explaining ability is far more than that of the medium-term momentum. We design a series of correlation experiments that are deemed significant to prove that the BM factor gradually becomes noticeable over time, which is also consistent with the traditional finance situation whereby the market tends to become an effective market, and the vision will gradually disappear.

For the BM effect, Fama and French (1992) studied all the stocks listed on NYSE, AMEX, and NASDAQ from 1963 to 1990 and found that the 1.53% combination of BM’s highest value (the value combination) had the lowest monthly yield (the combination of charm). Xiao Jun and Xu Xinzhong (2004) used Shanghai and Shenzhen stock market shares from June 1993 to June 2001 as a sample and calculated holding shares’ earnings in one year, two years, and three years to find that the BM effect does exist.

Finally, if we lack rigor in our research methods, we hope that other scholars in this area provide more in-depth study and more proof will emerge in the future.

The remainder of this paper is organized as follows: section2 presents the MC model and determines the parameters. Chapter 3 analyzes the BETA coefficients of the CAPM model based on the empirical analysis. In Chapter 4, the existence of the stock market, vision model, and empirical explanation are proposed. Chapter 5 analyzes the model’s inherent explaining mechanism. Chapter 6 gives a robustness test, and Chapter 7 summarizes the full text.

## Model

In developing the model, first, industry parameters must be determined. Over time, any company tends to become more like its peers. Any abnormal expected ROE (or excess expected return) will be gradually weakened due to the presence of industry competition. Equation (1) is also the core model for the anomaly interpretation.

### Sample selection and data sources

After obtaining the prediction model, the coefficients and other implied parameters of this model must be confirmed. The sample is selected from the quarterly training data (the sunken data), which is made up of 100 quarterly data points. The period was from the first quarter of 1980 to the fourth quarter of 1994 for all the US financial markets in DataStream. We implement the out-of-sample prediction from the first quarter of 2010 to the fourth quarter of 2015. At this time, we no longer use a sample of all industries but select five, and these five industries will run through the anomaly interpretation and robustness test. The main reason we gather data in this way is that collecting whole industry involves extensive work and the probability of error is greater.

### Empirical analysis

#### Sample regression analysis

Summary of model parameters

Regression coefficients | Implied parameters | |||||
---|---|---|---|---|---|---|

cons | bm | roe | k | w | μ | |

Panel A: Regression results summary | ||||||

5th percentile | -0.0119 | 0.013 | 0.1106 | 0.93523 | 0.707112 | 0.02003 |

25th percentile | 0.02114 | 0.0322 | 0.179 | 0.950202 | 0.817889 | 0.031925 |

Mean | 0.032162 | 0.045638 | 0.339625 | 0.964003 | 0.878349 | 0.056226 |

Median | 0.02985 | 0.0461 | 0.265 | 0.962121 | 0.893978 | 0.041286 |

75th percentile | 0.03772 | 0.0584 | 0.461 | 0.976566 | 0.938257 | 0.05825 |

95th percentile | 0.05589 | 0.07149 | 0.61 | 0.995 | 0.98653 | 0.12964 |

Standard deviation | 0.013273 | 0.017437 | 0.198346 | 0.017613 | 0.084486 | 0.181218 |

Panel B: Industry Coefficient | ||||||

Electricity | 0.03705 | 0.0453 | 0.114 | 0.964343 | 0.731332 | 0.041817 |

Equity investment instrument | 0.04606 | 0.0153 | 0.108 | 0.994646 | 0.89955 | 0.051637 |

Financial service | 0.04952 | 0.0423 | 0.46 | 0.967374 | 0.941658 | 0.091704 |

Fixed line telecommunications | 0.03535 | 0.0461 | 0.136 | 0.963535 | 0.763959 | 0.040914 |

Food and drug retail | 0.03188 | 0.057 | 0.97 | 0.952525 | 0.972041 | 1.062667 |

food producers | 0.05658 | 0.0288 | 0.51 | 0.98101 | 0.974212 | 0.115469 |

Forestry & paper | 0.03507 | 0.0402 | 0.384 | 0.969495 | 0.930503 | 0.056932 |

Gas, water, & multiple utilities | 0.06409 | 0.0225 | 0.59 | 0.987374 | 0.99193 | 0.156317 |

General industrial | 0.03798 | 0.0436 | 0.262 | 0.966061 | 0.879962 | 0.051463 |

General retailers | -0.01148 | 0.0622 | 0.53 | 0.947273 | 0.91966 | 0.024426 |

Health care equipment & services | 0.02878 | 0.0475 | 0.188 | 0.962121 | 0.817889 | 0.035443 |

Household goods & home construction | 0.03397 | 0.0512 | 0.117 | 0.958384 | 0.710426 | 0.038471 |

Industrial engineering | 0.02985 | 0.0554 | 0.277 | 0.954141 | 0.854701 | 0.041286 |

industrial metals & mining | 0.03686 | 0.0332 | 0.259 | 0.976566 | 0.910593 | 0.049744 |

Industrial transportation | 0.02691 | 0.0499 | 0.26 | 0.959697 | 0.860642 | 0.036365 |

Leisure goods | 0.01774 | 0.0632 | 0.354 | 0.946263 | 0.870677 | 0.027461 |

Life insurance | 0.04108 | 0.0187 | 0.461 | 0.991212 | 0.989546 | 0.076215 |

Media | 0.01825 | 0.0584 | 0.233 | 0.951111 | 0.81924 | 0.023794 |

Mining | 0.02114 | 0.0594 | 0.62 | 0.950101 | 0.938257 | 0.055632 |

Mobile telecommunications | 0.01277 | 0.0709 | 0.6 | 0.938485 | 0.918977 | 0.031925 |

No life insurance | 0.03772 | 0.0322 | 0.265 | 0.977576 | 0.916162 | 0.05132 |

Oil equipment & services | 0.02799 | 0.0433 | 0.192 | 0.966364 | 0.836456 | 0.034641 |

Oil & gas producers | 0.03311 | 0.0266 | 0.541 | 0.983232 | 0.981192 | 0.072135 |

Personal goods | 0.0288 | 0.0545 | 0.179 | 0.955051 | 0.78464 | 0.035079 |

Pharmaceuticals & biotechnology | -0.01425 | 0.072 | 0.38 | 0.937374 | 0.86246 | 0.022984 |

Real estate investment & services | 0.05535 | 0.0129 | 0.174 | 0.997071 | 0.957728 | 0.06701 |

Real estate investment trusts | 0.04822 | 0.0132 | 0.269 | 0.996768 | 0.981286 | 0.065964 |

Software & computer services | 0.02563 | 0.0593 | 0.56 | 0.950202 | 0.929461 | 0.05825 |

Sport services | -0.01356 | 0.0769 | 0.222 | 0.932424 | 0.75965 | 0.017429 |

Technology hardware & equipment | 0.02293 | 0.0613 | 0.138 | 0.948182 | 0.707112 | 0.026601 |

Tobacco | 0.02047 | 0.0529 | 0.356 | 0.956667 | 0.893978 | 0.031786 |

Travel & leisure | 0.02875 | 0.0442 | 0.159 | 0.965455 | 0.80129 | 0.034185 |

Table 1 shows that the coefficient and implicit parameters between industries differ, which suggests that every industry’s response to external change varies because of its unique characteristics. For most industries, the forecast returns are consistent with actual earnings; that is, the k value is large enough. Some industries, such as gas, water, and multiple utilities; life insurance; financial services; and equity investment instruments have slightly larger coefficients. Therefore, their returns are vulnerable to outside influences. Industries such as food and drug retail are different. ROE coefficient values are close to one implying returns are easily influenced.

#### Expected returns analysis

After determining the coefficient, we calculate the out-of-sample return. First, we need to predict in-sample return before interpreting vision. The reason is the following: first, the anomaly excess return is also a part of the actual benefit; second, predicting ability in 3, 12, 24, and 36 months is mainly possible because long-term reversal typically occurs between three to five years of holdings, and the medium-term momentum typically occurs between three to 12 months. Thus, if the model can predict returns within three years or less, it is likely that the MC model can explain the above two anomalies better.

Summary of expected returns

1Q ahead | 4Q ahead | 8Q ahead | 12Q ahead | Long term | LT-1Q difference | 12Q-1Q difference | |
---|---|---|---|---|---|---|---|

Panel A: Expected log returns | |||||||

5th | -0.4794 | -0.5047 | -0.83602 | 0.79245 | 0.3987 | -0.80482 | -0.5963 |

25th | -0.2079 | -0.3625 | 1.0216 | 0.9635 | 0.7758 | -0.02842 | -0.0692 |

Mean | 0.7808 | 0.56554 | 1.4438 | 1.3871 | 1.3511 | 1.58205 | 0.721 |

Median | 0.82983 | 0.5757 | 1.6883 | 0.78332 | 1.0587 | 1.4608 | 0.503 |

75th | 0.89856 | 0.8399 | 1.7164 | 1.89945 | 2.0655 | 1.249 | 0.9803 |

95th | 0.9563 | 1.56075 | 1.952 | 2.90988 | 2.7431 | 2.0729 | 1.5028 |

Standard deviation | 0.633536 | 0.6896 | 1.0295 | 0.83071 | 0.868 | 0.7052 | 0.74976 |

Panel B: Realized log returns | |||||||

5th | -0.41356 | -0.1694 | -0.17981 | -0.97937 | 0.3842 | ||

25th | -0.17805 | -0.0497 | -1.044 | 0.20461 | 1.0284 | ||

Mean | 0.74912 | 1.3646 | 1.1662 | 1.6952 | 1.2974 | ||

Median | 1.07651 | 0.9445 | 1.4047 | 2.00348 | 1.3002 | ||

75th | 1.5181 | 1.87121 | 2.3969 | 3.2014 | 1.5355 | ||

95th | 1.8595 | 2.1495 | 3.86763 | 3.5383 | 2.4085 | ||

Standard deviation | 0.910017 | 0.967892 | 1.675042 | 1.7374 | 0.662228 |

### Predictive ability regression tests

By expecting cross-sectional property, we need to verify the MC model’s estimated reliability. We focus on companies’ cross-sectional property in the three-year period.

Under the condition of δ0 = 0,δ1 = 1, there will be an absolute true estimate of the expected return for any holding period. Thus, by meeting such a benchmark as much as possible, when δ1 is more important, the expected value is closer to the true value.

Revenue return

Panel A: Regression parameter summary | ||||||||
---|---|---|---|---|---|---|---|---|

Data | Regression coefficients | Implied parameters | ||||||

cons | bm | roe | F | k | w | μ | ||

5th percentile | 0.014 | 0.045 | 0.259 | 70.62 | 0.904 | 0.620 | 0.014 | |

25th percentile | 0.018 | 0.048 | 0.325 | 145.24 | 0.952 | 0.647 | 0.017 | |

Mean | 0.022 | 0.050 | 0.430 | 359.70 | 0.910 | 0.657 | 0.023 | |

Median | 0.025 | 0.058 | 0.539 | 879.02 | 0.948 | 0.800 | 0.041 | |

75th percentile | 0.029 | 0.065 | 0.872 | 1426.60 | 0.963 | 0.889 | 0.059 | |

95th percentile | 0.035 | 0.094 | 0.890 | 1552.22 | 0.976 | 0.913 | 0.064 | |

standard deviation | 0.027 | 0.054 | 0.205 | 819.93 | 0.015 | 0.090 | 0.036 | |

3M (1) | 12M (2) | 24M (3) | 36M (4) | 3M (5) | 12M (6) | 24M (7) | 36M (8) | |

E[r | 0.717 | 0.643 | 0.5828 | 0.438 | 0.65284 | 0.5972 | 0.50284 | 0.4028 |

(0.073) | (0.0615) | (0.0295) | (0.0103) | (0.0624) | (0.040) | (0.0105) | (0.004) | |

Cons | -0.0343 | 0.0368 | 0.0329 | 0.0319 | 0.0286 | 0.3058 | 0.2528 | 0.2485 |

(0.063) | (0.0132) | (0.0202) | (0.2840) | (0.0468) | (0.008) | (0.0120) | (0.025) | |

Number of observations | 2851 | 2212 | 2105 | 1776 | 2851 | 2212 | 2105 | 1776 |

Fixed effects | no | no | no | no | yes | yes | yes | yes |

Adj.R | 0.032 | 0.031 | 0.025 | 0.030 | 0.0193 | 0.0284 | 0.5020 | 0.1598 |

## CAPM model parameters

After determining the basic coefficients of the MC model, we must determine the coefficients of the CAPM model. To ensure the consistency of the data, before predicting the CAPM model coefficients, we still use the historical quarterly data in DataStream as sample data although the calculation method will slightly differ. The stock returns rate and the current stock price are directly substituted in the one-way linear regression model. When the company's stock is subjected to a linear regression model, we calculate the average value of all the companies in the industry to obtain the industry model coefficient.

### Model

where rf,t is the risk-free return rate. Typically, similar foreign research replaces the risk-free rate with the interbank interest rate or short-term treasury interest rates. In this section, we use short-term treasury interest rates. On the other hand, βi (BETA) is a systematic risk coefficient, and E(rm) represents the market portfolio rate of return. In practical application, we use the first-60-months average yield of one of the stock replacements E(rm). E(rm) − rf or the market risk premium is different from the market portfolio yield and risk-free rate of return. If α is 0, the CAPM model can explain the profit of the investment strategy. The investment strategy does not have excess returns. If α is significantly greater than zero, this indicates that the strategy has excess returns unexplained by the CAPM model.

### Descriptive statistics

Summary data for CAPM model parameters

Regression coefficients | |||
---|---|---|---|

α | β | ɛ | |

Panel A: Model Coefficients Summary | |||

5th percentile | 0.0453 | 0.8711 | 0.0377 |

25th percentile | 0.0876 | 0.9875 | 0.0638 |

Mean | 0.4204 | 1.2122 | 0.1329 |

Median | 0.4147 | 1.1988 | 0.0943 |

75th percentile | 0.6833 | 1.4300 | 0.1678 |

95th percentile | 0.8456 | 1.5617 | 0.3370 |

Standard deviation | 0.2954 | 0.2578 | 0.1137 |

Panel B: Industry parameters | |||

Electricity | 0.8474 | 1.0670 | 0.0394 |

Equity investment instrument | 0.7250 | 1.4428 | 0.1804 |

Financial service | 0.0186 | 1.4257 | 0.2791 |

Fixed line telecommunications | 0.8296 | 0.9953 | 0.2163 |

Food and drug retail | 0.7390 | 0.9877 | 0.2578 |

Food producers | 0.4911 | 0.8347 | 0.0356 |

Forestry & paper | 0.6136 | 1.5032 | 0.0896 |

Gas, water, & multiple utilities | 0.5107 | 1.4082 | 0.0933 |

General industrial | 0.0622 | 1.5265 | 0.0476 |

General retailers | 0.6694 | 0.9696 | 0.0485 |

Health care equipment & services | 0.3203 | 1.3564 | 0.1079 |

Household goods & home construction | 0.1835 | 0.8694 | 0.0759 |

Industrial engineering | 0.0832 | 1.4800 | 0.0654 |

Industrial metals & mining | 0.3524 | 0.9059 | 0.0953 |

Industrial transportation | 0.0986 | 1.3895 | 0.2684 |

Leisure good | 0.0888 | 1.3269 | 0.1080 |

Life insurance | 0.8441 | 1.7830 | 0.1584 |

Media | 0.4231 | 1.2069 | 0.0705 |

Mining | 0.0614 | 1.0754 | 0.0959 |

Mobile telecommunications | 0.2658 | 1.4790 | 0.0896 |

No life insurance | 0.4000 | 0.8725 | 0.0479 |

Oil equipment & services | 0.0257 | 1.5329 | 0.0759 |

Oil & gas producers | 0.0671 | 1.0304 | 0.1860 |

Personal goods | 0.4062 | 1.1343 | 0.0695 |

Pharmaceuticals & biotechnology | 0.7483 | 0.9581 | 0.0499 |

Real estate investment &services | 0.0838 | 1.5968 | 0.5369 |

Real estate investment trusts | 0.0671 | 1.0343 | 0.1636 |

Software & computer services | 0.8521 | 1.3055 | 0.0968 |

Sport services | 0.7386 | 1.1907 | 0.0590 |

Technology hardware & equipment | 0.6631 | 0.9056 | 0.1074 |

Tobacco | 0.5633 | 0.9867 | 0.0284 |

Travel & leisure | 0.6084 | 1.2080 | 0.4078 |

Table 4 shows all the data of the estimated parameters (7). The predicted constants α are 0.0453, 0.0876, 0.4204, 0.4147, 0.6833, and 0.8456 respectively. Except for a few industries, the constant α in Table 4 is significantly not zero at the 1% level. This result is consistent with our daily observations that there is a large part of the returns that we cannot explain with conventional ideas. This proves that excess return does exist in the US stock market, and this excess return cannot be explained by the CAPM model. If we explain it from the perspective of behavioral finance, the excess returns are due to the existence of stock market anomalies. Therefore, this article becomes even more meaningful when interpreting the stock market vision.

## Verification of existence in the stock market

Through intuitive observation of Fig. 1, mid-term momentum is obvious from 1980 to 2010. In Fig. 2, only the two stages of 1992 to 1995 and 2007 to 2010 show an obvious long-term reversal phenomenon. This is consistent with the timing of two major events in US financial history. At other stages, it is difficult to find the presence of anomalies though intuitive observation. This shows on a cursory level that stock market visions do exist in the US market and have a more significant impact during the special period.

### Verification method and sample data

#### Verification method

Medium-term momentum and long-term reversal will generate excess returns, which are contrary to the effective market hypothesis. According to De bondt and Thaler (1985) research methods and conclusions, we use the collected historical data to build the portfolio. Samples are arranged in ascending or descending order (ascending or descending order does not affect the result) and then the data are divided into 10 samples; each has the same number of shares. We define the combination with the lowest returns as L and the highest returns as the winner portfolio called W. The momentum strategy involves buying the winner portfolio at the same time as selling the loser portfolio to obtain profit, which can be expressed as W-L. The contrarian effect is the opposite. According to the effective market hypothesis, the W-L portfolio profit should be zero; that is, following the principle of no arbitrage. However, if the stock price fluctuation truly has momentum or reverse effect, these portfolios will generate profit or loss within a certain period. This phenomenon has been called stock market anomalies.

The constructs are arranged according to the return rate in the forming period (denoted by F). We then calculate the returns of the respective combinations in different holding periods (denoted by H). Under the profitability of the FH investment strategy, within t time, investors first sell the former F-stage loser portfolio and then buy the equal amount of the winner portfolio holding the W-L portfolio for H period. Due to the medium-term momentum and long-term reversal’s particularity, the time is limited to three to 12 months of the medium-term momentum and three to five years of long-term reversal. Considering that previous studies often use 3, 6, 9, 12, 18, 24, 30, and 36 months, we choose the formation and duration period as follows: 3, 6, 9, 12, 18, 24, 30, 36, and 48. Ultimately, we produce 100 trading strategies. The specific methods and research processes are as follows:

To distinguish between the top 10 and the bottom 10,we define J as the number of months of the top 10 stock holdings and j as the number of months of the holding period of the last 10 stocks, and the values are equal. For example, wp = (r1,4 + 1) ∗ (r1,5 + 1) ∗ (r1,6 + 1) − 1 in the (3, 3) combination of the first stock. Therefore, we obtain a (3, 3) combination of the first group of wp and the same for lp. Followed by April, May, and June for the formation of the period, we first find the first July, August, and September of the wp and lp. We return to step (2), and so on, until we calculate all combinations of wp and lp in the (3, 3) combination and, finally, calculate a total of 100 combinations.

where x̅ is the mean of wp − lp, s is the standard deviation of wp − lp, and the number of samples n is the number of wp − lp.

Then, we perform the significant test of the data obtained to meet the significance of the test requirements of the wl analysis. If wl > 0, that means there is a significant momentum in the period. If wl < 0, there is a clear reversal effect in the financial market.

#### Sample data

Because the explanations and the recommendations are based on industry, we still select five industry historical data from the 32 industries, protecting the number of observations and laying the foundation for follow-up work. The inspection time was set at the beginning of 1996, and 100 stocks were selected from listed companies at the beginning of 1996 (20 listed companies in each industry). In this way, we construct a loser and winner portfolio with only 10 deals. We take all the sample monthly closing prices from 1996 January to 2015 December.

### Empirical results

One hundred groups of wp, lp, wl, t value summary table

Hold period q Forming period p | 3 months | 6 months | 9 months | 12 months | 18 months | |

3 months | wp | 0.0385 | 0.0392 | 0.0370 | 0.0356 | 0.0665 |

lp | -0.0255 | -0.0232 | -0.0221 | -0.0215 | -0.0446 | |

wl | 0.063969※ | 0.0623610※ | 0.059098※ | 0.057092※ | 0.111089※ | |

t | 6.9500 | 6.1090 | 5.7480 | 5.5560 | 5.4110 | |

6 months | wp | 0.0486 | 0.0407 | 0.0352 | 0.3688 | -0.2683 |

lp | -0.0285 | -0.0182 | -0.0222 | 0.7132 | -0.7778 | |

wl | 0.0770677※ | 0.058856※ | 0.057340※ | -0.344455※ | 0.509538※ | |

t | 4.3075 | 2.6328 | 3.1859 | 4.8540 | 4.1248 | |

9 months | wp | 0.0238 | 0.0319 | 0.0247 | 0.0381 | 0.0580 |

lp | -0.0169 | -0.0155 | -0.0185 | -0.0262 | -0.0562 | |

wl | 0.0406283※ | 0.0474118※ | 0.0431439※ | 0.0642729※ | 0.1142328※ | |

t | 5.1079 | 3.1171 | -4.0875 | 5.8650 | 7.1266 | |

12 months | wp | 0.0387 | 0.0507 | 0.0332 | 0.0313 | 0.0625 |

lp | -0.0105 | -0.0129 | -0.0164 | -0.0146 | -0.0479 | |

wl | 0.04915※ | 0.063644※ | 0.049570※ | 0.045913※ | 0.1104 | |

t | 4.6594 | 0.9907 | 2.8650 | 6.8660 | 1.1066 | |

18 months | wp | 0.0390 | 0.0563 | 0.1897 | -0.1742 | 0.0485 |

lp | -0.0103 | -0.0106 | -0.0258 | -0.0255 | -0.0418 | |

wl | 0.049247※ | 0.066929※ | 0.202296※ | -0.151084※ | 0.0903 | |

t | 12.1355 | 3.8790 | 2.7135 | -4.8593 | 1.1042 | |

24 months | wp | 0.0498 | 0.0521 | 0.0542 | 0.0354 | 0.0289 |

lp | -0.0134 | -0.1464 | 0.0097 | 0.0005 | -0.0462 | |

wl | 0.063209※ | 0.1984872※ | 0.0444647※ | 0.0348749※ | 0.0751517※ | |

t | 3.3684 | 3.4058 | 2.2480 | 2.8288 | 6.1173 | |

30 months | wp | 0.0408 | 0.0319 | 0.0126 | 0.0097 | 0.0119 |

lp | -0.0491 | -0.0314 | -0.0302 | -0.0183 | -0.0187 | |

wl | 0.089843※ | 0.0633350※ | 0.0428105※ | 0.0280763※ | 0.0306 | |

t | 3.8345 | 2.8411 | 1.9135 | 3.8645 | 1.1342 | |

36 months | wp | 0.0909 | 0.0098 | -0.0043 | -0.0116 | -0.0280 |

lp | 0.0503 | 0.0410 | 0.0216 | 0.0148 | 0.0321 | |

wl | 0.0405775※ | -0.031161※ | -0.025845※ | -0.026374※ | -0.0601 | |

t | 2.7015 | 2.3710 | 4.7250 | 6.2186 | -1.1700 | |

48 months | wp | 0.1087 | 0.0964 | 0.0495 | 0.0211 | 0.0569 |

lp | -0.0208 | -0.0172 | -0.0118 | -0.0074 | -0.0299 | |

wl | 0.12959※ | 0.1136495※ | 0.0613 | 0.0284981※ | 0.0868 | |

t | 2.3094 | 2.0923 | 1.6293 | 2.2380 | -1.1901 | |

60 months | wp | 0.0103 | 0.0139 | 0.0153 | 0.0162 | 0.0171 |

lp | -0.0204 | -0.0167 | -0.0101 | -0.0090 | -0.0032 | |

wl | 0.0307244※ | 0.0306 | 0.0254 | 0.0252 | 0.0204 | |

t | -3.5518 | -1.8998 | -1.6082 | 1.2573 | -0.0508 | |

Hold period q Forming period p | 24 months | 30 months | 36 months | 48 months | 60 months | |

3 months | wp | 0.0591 | 0.0497 | -0.0286 | -0.0246 | -0.0612 |

lp | -0.0453 | -0.0441 | -0.0579 | 0.0221 | 0.1364 | |

wl | 0.10444※ | 0.093842※ | 0.0292616※ | -0.04673042※ | -0.1975691※ | |

t | 5.5660 | 5.7970 | 4.0500 | -6.3380 | -6.9130 | |

6 months | wp | 0.0690 | 0.0364 | -0.0425 | 0.0217 | 0.0292 |

lp | -0.0335 | -0.0551 | -0.0407 | -0.1476 | 0.1022 | |

wl | 0.102505※ | 0.091487※ | -0.001725※ | 0.1693044※ | -0.0730656※ | |

t | 4.1339 | 8.3676 | -3.3159 | 6.4333 | 2.4222 | |

9 months | wp | 0.0597 | 0.0467 | -0.0421 | 0.0054 | -0.0460 |

lp | -0.0442 | -0.0427 | 0.0397 | 0.1088 | -0.0912 | |

wl | 0.103965※ | 0.089413※ | -0.0818328※ | -0.1034639※ | 0.0452 | |

t | 6.1218 | 10.2500 | -3.3015 | -2.3915 | -1.9165 | |

12 months | wp | 0.0503 | 0.0450 | -0.0387 | -0.0693 | -0.0594 |

lp | -0.0404 | 0.0478 | 0.0383 | 0.0731 | -0.0612 | |

wl | 0.090685※ | -0.0028797※ | -0.076932※ | -0.1423882※ | 0.0018128※ | |

t | 5.1261 | -6.2303 | -3.9858 | -4.9017 | 2.1925 | |

18 months | wp | 0.0487 | 0.0393 | 0.0322 | -0.0609 | 0.0515 |

lp | -0.0437 | -0.0439 | -0.0396 | -0.0672 | 0.0517 | |

wl | 0.092433※ | 0.0832855※ | 0.0719 | 0.0063171※ | -0.00022※ | |

t | 2.3860 | 4.2167 | 1.2674 | 3.3433 | -2.0865 | |

24 months | wp | 0.0477 | 0.0446 | 0.0385 | -0.0723 | -0.0552 |

lp | -0.0200 | -0.0398 | -0.0326 | 0.0418 | 0.0564 | |

wl | 0.0676301※ | 0.0844540※ | 0.071153※ | -0.1141 | -0.1116 | |

t | 3.0767 | 10.1525 | 7.2020 | -1.2751 | -1.9976 | |

30 months | wp | 0.0291 | 0.0330 | 0.0318 | 0.0527 | -0.0463 |

lp | -0.0245 | -0.0399 | -0.0343 | 0.0604 | -0.0495 | |

wl | 0.0536 | 0.0729 | 0.0661 | -0.0077 | 0.0032618※ | |

t | 1.0736 | 1.0897 | 1.1173 | 0.8520 | 5.9153 | |

36 months | wp | -0.0217 | -0.0325 | -0.0293 | -0.0590 | 0.0477 |

lp | 0.0241 | 0.0175 | 0.0158 | 0.0647 | 0.0484 | |

wl | -0.0457920※ | -0.0500914※ | -0.045144※ | -0.1237 | -0.000688※ | |

t | -5.0737 | -8.0590 | -5.0440 | -1.1230 | -4.5070 | |

48 months | wp | 0.0445 | 0.0504 | 0.0401 | 0.2460 | 0.1566 |

lp | -0.0295 | -0.0460 | -0.0378 | -0.0732 | -0.0604 | |

wl | 0.0740122※ | 0.0965 | 0.0778 | 0.3192 | 0.2169 | |

t | -3.0458 | 1.0312 | 0.9965 | 1.0826 | -0.6063 | |

60 months | wp | 0.0255 | 0.0267 | 0.0211 | 0.0041 | 0.0044 |

lp | -0.0027 | -0.0024 | 0.0264 | 0.0600 | 0.0465 | |

wl | 0.0282 | 0.0291 | -0.0053 | -0.0558800※ | -0.0421593※ | |

t | 1.0402 | 1.0100 | -0.9474 | -4.0311 | -3.6525 |

Table 5 shows a clear reversal effect of the loser stock portfolio, particularly in the formation of 3, 6, and 18 months. The loser portfolio in the holding period of three to five years will have a significant positive return, which is a different conclusion than that obtained from the intuitive observations of Figs. 1 and 2. In Table 5, the wl are 0.063969, 0.0623610, 0.059098, 0.057092, 0.111089, 0.10444, 0.093842, 0.0292616, -0.04673042, and -0.1975691, respectively, at the forming period of three months, and all the results were significant. At the forming period of 48 months, the wl are 0.12959, 0.1136495, 0.0613, 0.0284981, 0.0868, 0.0740122, 0.0965, 0.0778, 0.319,2 and 0.2169. Among them, the results were significant for 3, 6, and 24 months. The loser and winner portfolios shown in Table 5 have obvious performance in the momentum effect. Through observation, we find that the momentum effect has significant performance through the three to nine months’ holding period. During the same period, the wl portfolio showed an increasing trend with an increase in the formation period. Therefore, we conclude that when the combination period and holding period become longer, the effect of the reversal and the momentum become less significant.

### CAPM model zero interpretation ability

The time interval t is one month, rmt is the return of the equal market index, and the return of the loser and winner portfolio is checked by (16). (17) is used to test the W-L portfolio (the same as L-W). The test results show that the BETA value can explain most of the change in the winner and loser portfolio earnings (above 75%). However, the W-L (or L-W) portfolio cannot be explained by this BETA. In the case of strategy 3-36, which brings out momentum returns, there is a positive return on the winner portfolio, a negative return on the loser portfolio, and this W-L portfolio’s BETA value is not significant at all. Therefore, the BETA, as a risk measure value, has no ability to explain the momentum and reverse profitability. This is theoretically affirmed by the CAPM model having zero interpretation on the medium-term momentum and long-term reversal.

## Vision interpretation

We determined all the coefficients required for the MC model and the CAPM model, and we also used the MC model to make a revenue forecast test for all data from January 2010 to December 2015. Then, following the development of the paper, we focus on the interpretation of medium-term momentum and long-term reversal.

### Building an explanatory model

First, we compare the CAPM model with the MC model, see (1) and (7). To facilitate understanding, we summarize two models that remove the constant term as follows:

MC model r_{i, t + 1} = β_{1}bm_{i, t} + β_{2}roe_{i, t} + ξ_{i, t + 1} (1)

CAPM model E(r_{i, t}) − r_{f, t} = β_{i}(E(r_{m}) − r_{f, t}) + ε_{i} (7)

E(ri,t) is the expected return, and ri,t+1is the expected log return. The coefficient of ROE reflects the profitability of different firms in the same industry, and the βi in the CAPM model is also an indicator of profitability.

Finally, according to the series of R′ data calculated by (22), we construct a new winner and loser portfolio and use the same method to verify the existence of the anomalies in the previous chapter to obtain the new returns value. Examining whether the mid-momentum and the long-term reversal are weakened, if γ contains these two return anomalies, then, through the new empirical analysis, the two anomalies will be well addressed. We offer an empirical test theory as follows. The sample data are expanded from December 2015 to June 2016.

Both the mid-momentum and the long-term reversal have weakened in the graphical trend in Figs. 1, 2, and 4, and the change in the monthly return is more obvious than the previous change. We show powerful graphical evidence in Figs. 5 and 6 and provide a more convincing empirical test.

### Empirical test

In addition to the processing of the sample, the interpretation process is consistent with the anomalies existence test. That is, the winner and loser portfolios are constructed based on the new return data.

Existential verification results

Hold period q Forming period p | 3 months | 6 months | 9 months | 12 months | 18 months | |

3 months | wp | -0.1384 | -0.0843 | -0.2944 | 0.2095 | -0.0283 |

lp | 0.0631 | 0.0295 | -0.0298 | -0.3943 | 0.3943 | |

wl | -0.2015※ | -0.1138※ | -0.2646※ | 0.6038※ | -0.4226※ | |

t | -7.5469 | -9.5765 | -2.3137 | -6.5177 | -2.1533 | |

6 months | wp | -0.0103 | -0.0384 | 0.0483 | 0.2048 | 0.1948 |

lp | 0.0384 | 0.2940 | -0.9387 | 0.2984 | -0.0849 | |

wl | -0.0487※ | -0.3324※ | 0.9870※ | -0.0936 | 0.2798※ | |

t | -4.3401 | -2.4090 | -4.4905 | -1.0605 | -1.1958 | |

9 months | wp | -0.2934 | -0.0206 | 0.0853 | -0.4832 | 0.0519 |

lp | 0.0238 | -0.0394 | 0.3085 | -0.5293 | -0.2085 | |

wl | -0.3172※ | 0.0188※ | -0.2232※ | 0.0461※ | 0.2604※ | |

t | -4.7922 | -8.5792 | -5.5912 | -3.7257 | -7.9083 | |

12 months | wp | -0.7083 | -0.3582 | 0.3849 | 0.2048 | -0.3028 |

lp | 0.2925 | 0.2490 | -0.0294 | 0.2084 | -0.0247 | |

wl | -1.0008※ | -0.6072※ | 0.4143※ | -0.0036※ | -0.2781※ | |

t | -5.0284 | -9.3962 | -7.2975 | -7.3374 | -9.8217 | |

18 months | wp | -0.9240 | 0.0108 | 0.1052 | -0.4937 | 0.2806 |

lp | 0.0284 | -0.2420 | 0.1083 | -0.0284 | 0.2874 | |

wl | -0.9524※ | 0.25284※ | -0.0030※ | -0.4653※ | -0.0068※ | |

t | -8.06059 | -7.7146 | -0.0415 | -2.7705 | -9.0339 | |

24 months | wp | -0.0183 | -0.0083 | 0.0398 | -0.0408 | -0.2084 |

lp | 0.0482 | 0.0029 | 0.0184 | -0.1294 | 0.1083 | |

wl | -0.0665※ | -0.0112 | 0.0214※ | 0.0886※ | -0.3167※ | |

t | -5.1487 | -0.5971 | -2.6853 | -8.9878 | -9.2583 | |

30 months | wp | -0.2948 | -0.1294 | 0.2948 | 0.0698 | -0.2949 |

lp | 0.0290 | 0.1084 | 0.0108 | -0.0908 | 0.2844 | |

wl | -0.3238 | -0.2378 | 0.2840 | 0.1606※ | -0.5793※ | |

t | -0.9548 | -0.7403 | -0.0126 | -5.6271 | -8.7080 | |

36 months | wp | 0.2084 | -0.2850 | 0.0203 | -0.3985 | 0.5082 |

lp | 0.0129 | 0.0940 | 0.2984 | -0.2084 | 0.2952 | |

wl | 0.1955 | -0.379※ | -0.2781※ | -0.1901※ | 0.213※ | |

t | -1.8446 | -3.6409 | -2.0442 | -3.9977 | -6.2831 | |

48 months | wp | -0.0024 | 0.2044 | -0.2049 | 0.0385 | -0.0108 |

lp | 0.0597 | 0.1094 | -0.2943 | -0.0044 | 0.2044 | |

wl | -0.0621※ | 0.095※ | 0.0894※ | 0.04285※ | -0.2152※ | |

t | -8.873 | -7.2558 | -8.5790 | -5.5865 | -6.3009 | |

60 months | wp | -0.0070 | 0.1939 | 0.1033 | -0.0188 | 0.4298 |

lp | -0.2934 | 0.0139 | -0.0210 | -0.1944 | 0.0039 | |

wl | 0.2864※ | 0.1800※ | 0.1243※ | 0.1756※ | 0.4259※ | |

t | -2.8769 | -5.2485 | -8.0858 | -6.7919 | -8.9324 | |

Hold period q Forming period p | 24 months | 30 months | 36 months | 48 months | 60 months | |

3 months | wp | 0.0428 | 0.2490 | 0.1932 | 0.3942 | 0.1038 |

lp | -0.0208 | -0.0283 | -0.0286 | -0.0246 | -0.0612 | |

wl | 0.06364※ | 0.27734※ | 0.22180※ | 0.4188※ | 0.1650※ | |

t | -9.5661 | -6.4948 | -4.5017 | -4.3875 | -6.5235 | |

6 months | wp | -0.0242 | 0.1084 | -0.3942 | 0.0329 | 0.2494 |

lp | 0.6092 | 0.0903 | -0.3934 | -0.2820 | -0.3028 | |

wl | -0.6334※ | 0.01812※ | -0.0087※ | 0.31494※ | 0.5522※ | |

t | -4.7564 | -2.3861 | -5.7409 | -9.4873 | -9.6719 | |

9 months | wp | 0.2058 | 0.5038 | 0.4846 | 0.2045 | 0.5028 |

lp | 0.4080 | -0.0920 | 0.2058 | 0.0385 | -0.3040 | |

wl | -0.2022※ | 0.5958 | 0.2788※ | 0.1660 | 0.8068※ | |

t | -7.3042 | -1.2111 | -7.6711 | -1.7727 | -2.5962 | |

12 months | wp | 0.3856 | -0.4941 | 0.3494 | 0.3842 | -0.0794 |

lp | -0.5927 | 0.3859 | 0.4928 | -0.3842 | -0.5938 | |

wl | 0.9783※ | -0.88※ | -0.1434※ | 0.7684※ | 0.5144※ | |

t | -7.2787 | -8.0722 | -2.9770 | -7.2284 | -5.0118 | |

18 months | wp | 0.1958 | -0.1858 | -0.0184 | 0.1540 | -0.1885 |

lp | 0.0593 | 0.0284 | -0.1084 | -0.3749 | 0.0563 | |

wl | 0.13653※ | -0.2142※ | 0.09※ | 0.5289※ | -0.2448※ | |

t | -9.0714 | -9.6582 | -3.3249 | -8.1674 | -4.2962 | |

24 months | wp | -0.0183 | 0.1098 | 0.1282 | 0.2853 | 0.0828 |

lp | 0.2945 | -0.2084 | -0.0173 | 0.2943 | -0.2084 | |

wl | -0.3128※ | 0.3182※ | 0.1455※ | -0.0090 | 0.2912※ | |

t | -3.5671 | -5.7649 | -3.3872 | -1.3965 | -7.8998 | |

30 months | wp | 0.3930 | -0.0109 | -0.0014 | 0.0194 | 0.1854 |

lp | -0.1050 | 0.0335 | 0.1042 | -0.1988 | -0.4851 | |

wl | 0.498※ | -0.0444 | -0.1056※ | 0.2182※ | 0.6705※ | |

t | -1.2578 | -1.7227 | -3.6256 | -5.0381 | -6.5537 | |

36 months | wp | 0.0189 | -0.3940 | -0.2840 | -0.0018 | -0.1030 |

lp | -0.2848 | 0.0592 | -0.2020 | -0.0188 | -0.0128 | |

wl | 0.3037※ | -0.4532※ | -0.0820※ | 0.0170 | -0.0902※ | |

t | -5.7673 | -8.9516 | -4.3568 | -0.61444 | -8.8585 | |

48 months | wp | 0.3095 | -0.0240 | 0.4085 | 0.2953 | 0.0044 |

lp | -0.1084 | 0.3848 | -0.0220 | -0.0140 | 0.0199 | |

wl | 0.4179※ | -0.4088※ | 0.4305※ | 0.3093※ | -0.0155 | |

t | -1.7304 | -5.2197 | -2.3661 | -3.6702 | -1.1061 | |

60 months | wp | 0.1088 | -0.0053 | -0.2420 | -0.0110 | -0.0399 |

lp | 0.2842 | -0.0910 | 0.2985 | 0.0503 | 0.0018 | |

wl | -0.1753※ | 0.0857※ | -0.5405※ | -0.0613※ | -0.0417※ | |

t | -2.5214 | -2.6269 | -5.2042 | -9.6926 | -4.9969 |

From the described empirical study, we draw two conclusions: first, the MC model has a certain ability to predict income, which is excellent news for the future of the US stock market. Second, based on the CAPM model, we obtain a new model with explanatory ability for medium-term momentum and long-term reversal. According to the empirical results, this model can explain the medium-term momentum and shows weak ability, but not completely zero, for long-term reversal. In the existing study of the US market, there are few models that can explain both mid-term momentum and long-term reversal.

### Correlation analysis

Since we have verified that the MC model does exhibit some explanatory power for mid-momentum and long-term reversal, an ensuing problem arises: whether the BM or ROE is a factor in the interpretation of the model? Given this question, we conducted the following tests.

First, we remove the BM factor and ROE factor. Second, we repeat the same process as described above. The reconstructed model with only the BM factor or ROE factor can explain the two stock market anomalies, but the ability of the ROE factor is weaker than the model with only the BM factor.

As the two company basic indicators have some explanatory power concerning the vision, we need to determine whether there is a correlation between them and between ROE, BM and BETA.

It has been proven that there is a correlation between the current book value (the reciprocal of the book market ratio) and the current ROE, and the current book value also contains more information on the future ROE compared to the current ROE, which will cause change in the ROE. Additionally, Richard and Jeong (1997) tested the correlation between the ROE and BETA values and found that the coefficients were negative. This indicates that there is no correlation between the two values. Next, we must verify the relevance of the BM factor and the BETA and the hybrid correlation between the three.

Cross-sectional regression with BM as the dependent variable and BETA as the independent variable year by year

Cons | BETA | Adj.R | |
---|---|---|---|

2010 | 0.2554 | -0.1546 | 0.0655 |

2011 | 0.4229 | -0.2986 | 0.0055 |

2012 | 0.3500 | -0.2014 | 0.0204 |

2013 | 0.4476 | -0.1012 | 0.0253 |

2014 | 0.2759 | -0.0281 | 0.0062 |

2015 | 0.3939 | -0.2399 | 0.0772 |

A comparison of the results of year-round mixed regression with account-to-market ratio

Panel A: No dummy variables | ||||||

Independent variable | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |

Cons | 0.8302 | 1.2207 | 1.2830 | 0.7492 | 0.9298 | 1.0593 |

BETA | -0.0824 | -0.0240 | -0.3023 | -0.0427 | -0.6222 | -0.3812 |

ROE | 1.1221 | 1.8324 | 0.8066 | 1.4899 | 1.0270 | 1.9774 |

Panel B: There are dummy variables | ||||||

Independent variable | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |

Intercept | 0.7045 | 0.9152 | 0.6048 | 0.5362 | 0.5065 | 1.0709 |

BETA | -0.0783 | -0.0656 | -0.1084 | -0.0936 | -0.0676 | -0.0905 |

ROE | 1.3985 | 1.7643 | 1.3274 | 1.5536 | 1.5359 | 1.7982 |

In the mixed regression in Table 8, the BETA coefficient is less significant, and the fluctuation varies greatly compared to the regression results in Table 7. After adding the industry dummy variable, we found that the coefficient of BETA has no significance. This shows that company risk impacts the BM ratio. Thus, the industry's special factors capture risk more effectively than BETA.

Overall, ROE and BM do not correlate with BETA, and BM is less relevant to BETA. But BM and ROE are related, and BM will cause change in the ROE. This leads to the following conclusion: the BM factor is the fundamental reason that models can explain the medium-term momentum and long-term reversal. In other words, the BM effect does have an impact on medium-term momentum and long-term reversal.

### Test conclusions

Through the previous interpretation of the mid-momentum and the long-term reversal of the two anomalies, we confirm that BM, as an influencing factor, does have some explanatory power of the two visions in the US stock market. On the other hand, the interpretation also explains that the BM effect does cause part of the mid-momentum and long-term reversal to generate excess returns. The BM effect can only partially explain the two visions. There is no in-depth study on this subject, and it is not the focus of this paper. However, we believe that this issue will create a meaningful research direction. Of course, if there is change in the future, we would consider conducting more detailed research. Additionally, BM as a model impact factor is one for which the sample data are easy to obtain and calculate, which is a rare advantage.

For the model capacity’s difference with respect to mid-momentum and long-term reversal, we speculate that the reason may be that the book value and the market value are infinitely close to the same mean when the time lengthens.

Overall, the research results are of great importance. First, the MC model has a good ability to predict earnings, but company's basic data is not easy to obtain. The explaining of stock mid-momentum and long-term reversal is convenient.

For the model’s ability to predict and explain, first, we identify whether the model's capabilities are limited to return forecasts. Second, we identify whether this conclusion can be applied to other financial indicator forecasts. In other words, we identify whether the principles used in MC model construction can be exploited in different aspects of research in the future. Third, does this new model only apply to the above two types of visions, or is it possible to have explanatory power for other stock market anomalies? If the new model is applied to other stock market visions, what will be the consequence? This study only presents ideas, and future practical application is required. However, this new model will have deep significance for future research.

### Research prospects

Our study uses a newly constructed model to prove the existence of medium-term momentum and long-term reversal, but it also obtains the intrinsic relationship between the BM factor and these two visions. This is undoubtedly a great improvement in the field of financial market vision research. Based on this research, the author suggests that the following points should be discussed in depth.

First, the US market has significant mid-term momentum and long-term reversal. China, as a representative developing country, may have the same characteristics, which is contrary to the stated effective market hypothesis. Second, the MC model is used to predict future earnings and to explain financial visions in this paper. Thus, we can infer whether this model has good ability in other financial markets and even in other nonfinancial sectors. Future discussion will be based on existing research. Third, since the BM factor does have a considerable internal relationship with medium-term momentum and long-term reversal, the BM factor also presents some explanatory power for other visions in the financial market. This research question will become our future main research direction.

## Robustness test

### Control the sample age

When the model is calculated for the model coefficients and results, the sample used is the initial training sample from 1980 to 1994. When we use the complete model to test the performance of quarterly log returns, we use a sunken sample from June 2010 to June 2016. According to the empirical analysis conducted by Kelly and Pratt (2013), we need to consider the estimated deviation of the expected return due to the different sample. Therefore, to avoid this deviation, we choose the sample data from January 1996 to December 2009, which is a set of sample data at different time intervals to test the sample sensitivity.

### Use roei,t−1 instead of roei,t

The next step concerns the choice of an auxiliary variable roei,t, which may lead to unreliability in the test results. That is, in the process of using the log ROE(roei,t) to replace the expected log ROE(hi,t)of the next period, it is likely that the MC model will not agree with the estimation of β2. If the final test result is moderate, this indicates that the lag value of ROE is a useful auxiliary variable, and this auxiliary variable can be used to weaken potential bias in the coefficient estimating process.

Supplementary variable estimation results

Panel A: Regression parameter summary | ||||||||

Data | Regression coefficients | Implied parameters | ||||||

cons | bm | roe | F | k | w | μ | ||

5th percentile | 0.01378 | 0.01967 | 0.1149 | 93.1769 | 0.9374 | 0.7144 | 0.0248 | |

25th percentile | 0.0254 | 0.0417 | 0.1557 | 257.869 | 0.9515 | 0.8075 | 0.0346 | |

mean | 0.0323 | 0.0478 | 0.4942 | 765.484 | 0.9618 | 0.8563 | 0.0474 | |

median | 0.0317 | 0.0495 | 0.3615 | 503.141 | 0.9601 | 0.8704 | 0.0422 | |

75th percentile | 0.0375 | 0.0581 | 0.3813 | 1040.069 | 0.9679 | 0.9255 | 0.0519 | |

95th percentile | 0.0558 | 0.0719 | 0.6032 | 2276.989 | 0.9904 | 0.9492 | 0.0865 | |

standard deviation | 0.01386 | 0.0174 | 1.1761 | 789.7891 | 0.0176 | 0.0849 | 0.0212 | |

Panel B: Return of Regressions | ||||||||

3M (1) | 12M (2) | 24M (3) | 36M (4) | 3M (5) | 12M (6) | 24M (7) | 36M (8) | |

E[r | 0.689 | 0.636 | 0.556 | 0.521 | 0.6028 | 0.569 | 0.4902 | 0.3502 |

(0.013) | (0.0103) | (0.0295) | (0.0615) | (0.013) | (0.0098) | (0.0208) | (0.0421) | |

Cons | 0.0343 | 0.0368 | 0.0329 | 0.0319 | 0.2492 | 0.22 | 0.21 | 0.195 |

(0.063) | (0.0132) | (0.0202) | (0.284) | (0.0492) | (0.0107) | (0.0150) | (0.254) | |

Number of observations | 1681 | 1232 | 1025 | 957 | 1681 | 1232 | 1025 | 957 |

Fixed effects | no | no | no | no | yes | yes | yes | yes |

The F statistic results for the fifth quartile were 93.1769, and the average quartile was 765.4844. The coefficient of roei,t increased from the least squares estimate 0.339625 (0.265) to the average score (median) of 0.494119 (0.3615). This result indicates that the results of the least squares estimation may be affected by the measurement error, resulting in an increase in the ROE coefficient estimation. In contrast, the coefficient variance changes from 0.198346 to 1.176139, almost 10 times that of the previous result. Thus, as previously proposed, there is a trade-off between the estimates of bias and efficiency. The constant coefficient estimates will increase mutations. The increase in all estimated coefficients’ anomalies will inevitably lead to anomalies in the implicit model parameters. However, this can be reduced by adjusting the implicit persistence parameter (0.9999). The mean (median) of the model estimation parameters indicates that the long-term unconditional expectation decreases from the OLS estimates 0.056226 (0.041286) to 0.047367 (0.042206). The persistence parameter value corresponding is from 0.964003 (0.962121) to 0.961834 (0.960101). The mean (median) of implied persistence parameters is still relatively constant for log expected returns.

Panel B of Table 9 shows the re-processing of the results of Table 3, corresponding to the panel A auxiliary variable estimation results. "***", "**", "*" represent significance levels of 10%, 5%, and 1%, respectively. The fourth auxiliary variable roe(i, t+1) is used to predict the future expected return, and the others do not change significantly. Within three-years-ahead time conditions, we look for the return where the cross-section and the industry both have significant predictability. The log expected returns of the coefficients are 0.689 (0.6028), 0.636 (0.569), 0.556 (0.4902), and 0.521 (0.3502) for the 3, 12, 24, and 36 months, respectively. Under the industry fixed effect, each coefficient at a significance level of 1% is clearly not zero. Although they are still important, these coefficients are consistently lower than the OLS-based estimates.

In general, the use of roei,t−1 reduces the potential measurement estimation error in addition to the noise generated by the general parameter estimation. Therefore, the OLS estimate based on roei,t−1 may reduce the effective loss in the estimation process, which is also a motive to employ a more accurate model to forecasting returns in the first stage.

### Seasonal return

We choose the quarterly data rather than the month or year data at first, mainly because of the relatively modest amount of data. The study does not reveal excessive deviation because the time is too long or too short. However, using the quarterly data also has potential problems. Thus, we want to test the sensitivity of the results for the quarterly data and consider the potential of parameter estimation measurement error due to seasonal quarterly earnings.

For example, for the end of June 2010, we use the annual known logarithm return and observable annual BM and ROE to do a prediction at a time span selection from June 30, 2009 to June 30, 2010. This is the same as before using training samples from the years 1980 to 1995. The other estimates are similar to the quarterly earnings estimates method based on OLS.

Year data of least squares estimation results

Panel A: parameter estimation summary | |||||||

Data | Regression coefficient | implicit parameter | |||||

cons | bm | roe | F | k | w | μ | |

5th percentile | 0.013858 | 0.019755 | 0.12185 | 108.91885 | 0.943712 | 0.722926 | 0.01988 |

25th percentile | 0.024605 | 0.039525 | 0.16875 | 180.5975 | 0.948106 | 0.837972 | 0.033205 |

mean | 0.034328 | 0.048013 | 0.292219 | 1391.13566 | 0.961604 | 0.859725 | 0.053624 |

median | 0.032565 | 0.05195 | 0.2425 | 271.368 | 0.957626 | 0.867538 | 0.04623 |

75th percentile | 0.041725 | 0.061375 | 0.372 | 2300.75 | 0.970177 | 0.924935 | 0.058376 |

95th percentile | 0.06029 | 0.065725 | 0.608 | 5129.3 | 0.990146 | 0.952434 | 0.125165 |

standard deviation | 0.01578 | 0.0166 | 0.17457 | 1948.64092 | 0.016768 | 0.080108 | 0.036647 |

Panel B:Return regressions | |||||||

12M (2) | 24M (3) | 36M (4) | 12M (6) | 24M (7) | 36M (8) | ||

E[r | 0.504 | 0.462 | 0.34 | 0.482 | 0.405 | 0.315 | |

(0.0694) | (0.026) | (0.03) | (0.0272) | (0.0197) | (0.116) | ||

Cons | -0.033 | 0.027 | 0.185 | 0.205 | 0.273 | 0.302 | |

(0.024) | (0.055) | (0.077) | (0.0429) | (0.056) | (0.0207) | ||

Number of observations | 1254 | 1162 | 936 | 1254 | 1162 | 936 | |

Fixed effects | no | no | no | yes | yes | yes | |

Adj.R2 | 0.002 | 0.005 | 0.022 | 0.0102 | 0.009 | 0.0402 |

The MC model’s robustness test overall shows that the model is not very sensitive. Whether it adds a new auxiliary variable, changes the data age selection or the quarterly data to the annual data, the MC model has shown considerable stability. Thus, the model is not affected by too many external changes.

## Conclusion

### Progress and innovation

Although there are studies on stock market visions, this paper finds unexpected results in the research view and research methods. First, the focus of this article is to prove that medium-term momentum and long-term reversal exists and can be explained. Some studies prove that there are two types of visions. However, studies that explain the two visions are few. Second, the main research method we use in this study is the experimental method. We use the MC model with the BM factor, relying on a large number of sample data to prove the existence of medium-term momentum and long-term reversal. Additionally, we explain the existence of two types of visions. This research method is rare in the literature, which emphasizes the uniqueness of this study.

### Analysis conclusion

The mid-momentum and long-term reversal of stock returns are two typical stock market anomalies that play a key role in the process of resource allocation. Explaining these two anomalies means that they can predict the excess returns that they generate providing greater autonomy and greater benefits with respect to investment choices.

This paper considers the difference between the CAPM model and the MC model, and thus introduces a novel and relatively simple model to explain mid-momentum and long-term reversal. The model has some explanatory power for both visions, and the ability to explain the mid-momentum is improved compared to other models.

After defining the model’s ability to interpret the vision, the intrinsic mechanism of this interpretation is explored. Through a correlation analysis of several influencing factors in the model, we find that BM plays a key role in the interpretation of visions. Thus, we verify that the BM factor explains the mid-momentum and the long-term reversal, and the mid-momentum is more easily explained.

For some less in-depth study problems, we summarize the following. First, the BM effect has always been considered by investors as a type of stock market anomaly, and it can be quantified. Thus, we regard it as a basic indicator to join the model. Furthermore, we consider that if the BM as an impact factor clarifying other stock market visions, what level of ability will it show? Second, the whole model is rigorously derived, but there may be some inevitable omissions, particularly the impact of residuals that we cannot remove. Moreover, when we interpret the two visions, we cannot calculate how much of a role the residual plays.

### Enlightenment to future research

We conclude that US stock market volatility forecasting is easier than before. Our studies play an irreplaceable role in this work. For mature markets, as in the United States, the emergence of the vision is inevitable. Therefore, considering the market factors when we study the vision will be meaningful. At minimum, this article provides some advancement based on the existing literature in explaining future visions. The suggested model can explain both the mid-term momentum and the long-term reversal, but it also finds the most fundamental explanatory factor that can interpret both visions.

## Declarations

### Funding

I follow the tutor to do two fund projects which is the National Social Science Fund Project (15BJY164) and the Ministry of Education Humanities and Social Sciences Fund Project (14YJA790034), respectively.

### Authors' contributions

LW contribution: He is the first author of this paper and provides the most of the views and ideas towords the whole article conception and innovation. ZJ contribution: She as the second author, mainly assists the first author to complete the construction and writing of entire paper. Both authors read and approved the final manuscript.

### Competing interests

The authors declare that we have no competing interests.

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

## Authors’ Affiliations

## References

- Barberis N, Shleifer A (1998) A model of investor sentiment.[J]. J Financ Econ 49(3):307.Google Scholar
- Baytas A, Cakisi N (1999) Do markets overreact: International evidence.[J]. J Bank Financ 23(7):1121–1144Google Scholar
- Chan C (1988) On the Contrarian Investment Strategy[J]. J Bus 61(2):147–163.Google Scholar
- Chan LKC, Jegadeesh N, Lakonishok J (1996) Momentum Strategies[J]. J Financ 51(5):1681–1713.Google Scholar
- Chen C, Wang Y (2017) Understanding the multifractality in portfolio excess returns [J]. Physica A: Statistical Mechanics and its Applications: 346–355Google Scholar
- Chen Q, Wang T (2003) The Inertial Effect of Chinese Stock Market: An Empirical Study Based on Industry Combination [J]. Contemp Financ Econ (12):48–50Google Scholar
- Chena J, Jiangb F, Liuc Y, Tud J (2017) International volatility risk and Chinese stock return predictability [J]. J Int Money and Finance:183–203Google Scholar
- Cheung JK, Chung R, Kim J-B (1997) The Profitability of Trading Strategies Based on Book Value and Earnings in Hong Kong: Market Inefficiency vs. Risk Premia[J]. J Int Financ Manag Account 8(3):204–233.Google Scholar
- Chui A, Timan S, Wei KCJ (2000) Momentum, ownership structure, and financial crises: An analysis of Asian stock market[J]. J Financ Quant AnalGoogle Scholar
- Daniel KD, Hirshleifer DA, Subrahmanyam A (1998) A Theory of Overconfidence, Self-Attribution, and Security Market Under- and Over-reactions[J]. Financ 53(5)Google Scholar
- Debondt WFM, Thaler R (1985) Does the Stack Market Over-react?[J]. J FinancGoogle Scholar
- Debondt WFM, Thaler RH (1987) Further Evidence on Investor Overreaction and Stock Market Seasonality[J]. J Financ 42(3):557–581.View ArticleGoogle Scholar
- Fama EF, French KR (1992) The Cross‐Section of Expected Stock Returns[J]. J Financ 47(2):427–465Google Scholar
- Fama EF, French KR (1996) Multifactor Explanations of Asset Pricing Anomalies[J]. J Financ 51(1):55–84View ArticleGoogle Scholar
- Gaunt C (2000) Overreaction in the Australian equity market: 1974–1997[J]. Pac Basin Financ J 8(3):375–398Google Scholar
- Griffin PA (2003) Got Information? Investor Response to Form 10-K and Form 10-Q EDGAR Filings[J]. Rev Acc Stud 8(4):433–460Google Scholar
- Hameed A, Ting S (2000) Trading volume and short-horizon contrarian profits: Evidence from the Malaysian market[J]. Pac Basin Financ J 8(1):67–84Google Scholar
- Hong H, Stein JC (1999) A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets[J]. J Financ 54(6):2143–2184Google Scholar
- Jegadeesh N, Titman S (1993) Returns to buying winners and selling losers: implications for stock market efficiency J. J Finance 48(1):65–91Google Scholar
- Kaul G, Conrad J (1993) Long-Term Market Overreaction or Biases in Computed Returns?[J]. J Financ 48(1):39–63Google Scholar
- Lee CMC, Swaminathan B (2000) Price Momentum and Trading Volume[J]. J Financ 55(5):2017–2069Google Scholar
- Lyle MR, Wang CCY (2015) “The cross section of expected holding period returns and their dynamics Apresent value approach”, J Financ Econ 116(2015):505–525Google Scholar
- Newey W, West K (1987) A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix[J]. Econometrica 55(3):703–08Google Scholar
- Penman SH (1991) An Evaluation of Accounting Rate-of-Return[J]. J Acc Audit Financ 6(2):233–255Google Scholar
- Wang Yong-hong, Zhao Xue-jun (2001) Empirical Analysis of Inertial Strategy and Inversion Strategy of China's Stock Market [J]. Securities Market Guide 6:28–34Google Scholar
- Xiao J, Xu X (2004) Empirical Study on Validity of Chinese Inversion Investment Strategy [J]. Econ Res (3)55–64Google Scholar
- Zhu Z (2003) Research on Momentum Profit and Reverse Profit of Chinese Stock Market [dissertation]Google Scholar