Evaluation of forecasting methods from selected stock market returns

Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification. There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making. Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices. However, there have been very few studies of groups of stock markets or indices. The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets. In this context, this study aimed to examine the predictive performance of linear, nonlinear, artificial intelligence, frequency domain, and hybrid models to find an appropriate model to forecast the stock returns of developed, emerging, and frontier markets. We considered the daily stock market returns of selected indices from developed, emerging, and frontier markets for the period 2000–2018 to evaluate the predictive performance of the above models. The results showed that no single model out of the five models could be applied uniformly to all markets. However, traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts.


INTRODUCTION
The evaluation of forecasting methods in the context of stock market returns is an essential aspect of financial analysis and investment strategy.This process involves assessing various predictive models to determine their accuracy and reliability in forecasting future stock prices or market trends.Different methods, ranging from traditional statistical models like ARIMA (Autoregressive Integrated Moving Average) and moving averages to more complex machine learning algorithms such as neural networks and decision trees, are utilized to predict market behavior.And the specific characteristics of the stocks or indices being analyzed.For instance, highfrequency trading requires extremely accurate short-term forecasts, while long-term investors might prioritize models that capture broader market trends over time.
This paper aims to compare and contrast the performance of different forecasting methods on selected stock market returns, providing insights into which models perform best under various conditions and why.This analysis is vital for investors and analysts seeking to optimize their strategies in the ever-evolving landscape of the financial markets.

REVIEW OF LITERATURE
Cheng Zhao (2023) Predicting stock prices is crucial for stock market investing.However, the intricacy of stock price variables has been extensively examined.Traditional techniques that use timeseries data for one stock lack a comprehensive view.TSRM uses stock transaction data to automatically classify stocks and determine stock link ages using a K-means algorithm.LSTM time series and graph convolution network (GCN) connection information are used to forecast stock prices.Compared to the baseline, the TSRM improved cumulative returns by 44% and 41% and reduced maximum drawdown by 4.9% and 6.6% in the Chinese Shanghai and Shenzhen stock markets.
Michael Biehl (2023) Accurate stock price forecasts require identifying essential factors that impact machine learning (ML) models.Stock market forecasting using ML, statistical, and deep learning approaches has been reviewed in many studies.No stock market forecasting survey has examined feature selection and extraction methods.This survey analyzes 32 feature study-ML research studies in stock market applications.We discuss some effective feature selection and extraction methods used in the articles' stock market assessments.We report and assess the performance of feature analysis and ML approaches.Other survey papers, stock market input and output data, and factor analysis are also available.For stock market applications, Mostafa Shabani (2023) Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series.This study introduces a new method for predicting the future state of synchronization of the dynamics of two financial time series.To this end, we use the cross recurrence plot analysis as a nonlinear method for quantifying the multidimensional coupling in the time domain of two-time series and for determining their state of synchronization.Daiyou Xiao (2022) Machine learning is a systematic and comprehensive use of computer algorithms and statistical models that is applied in many disciplines.Machine learning is mostly used to predict capital market prices in finance.This article used classical and machine learning methods to predict stock time-series data for linear and non-linear problems, respectively.First, New York Stock Exchange stock samples from 2010-2019 are gathered.Next, stock price and sub correlation are trained and predicted using the ARIMA and LSTM neural network models.Finally, we test the proposed model using several indicators and find that: Addressing these gaps could enhance forecasting reliability and effectiveness.

OBJECTIVES OF THE STUDY
The main goal of a security forecast project is to learn more about security forecasting.
The research may assist in the analysis of securities growth.
To analyze the performance and measure of securities volatility using MACD analysis.

RESEARCH DESIGN
The study design will use a quantitative methodology to assess forecasting techniques using particular stock market results.It would include gathering historical data from many market places over a considerable amount of time on prices, volumes, and economic variables related to the stock market.Thisdata will be subjected to a variety offore casting models, which will include hybrid approaches, machine learning techniques, and conventional statistical methods.Metrics in clouding accuracy, mean absolute error(MAE),and root means quire error (RMSE)will be used to  The 3ema and 5ema were both moving upwards, which indicates that the trend was bullish.
 The Macd was also moving up wards, which confirms the bullish trend.
Overall, the stock price data for ADF FOODS is bullish.The stock price has been trending up wards and the technical indicators are all pointing to further gains. The 3ema and 5ema were both moving upwards,which indicates that the trend was bullish  The stock price experienced a sharp decline in March 2020,which was likely due to the COVID-19 pandemic.
 The stock price has since recovered and is currently trading above the 3ema and 5ema. The 3ema and 5ema were both movingup wards,which indicates that the trend was bullish.

ADF FOODS
Overall, the stock price data for ADF FOODS is bullish. The 3ema and 5ema were both moving upwards in the beginning of 2022, which indicate sthat the trend was bullish.
 However, the Mac started to decline in January2022, which in dictates that the trend was starting to become bearish.
 The stock price has been declining since January2022, and is currently trading below the 3 ema and 5ema.Overall, the stock price data for ADF FOODS is bearish.Overall, the stock price data for ITC is bearish.The stock price has been trending downwards and the technical indicators are all pointing to further losses.

HYPOTHESIS
Overall, the stock price data for ITC is mixed.There are some positive signs, such as the upward trend in the 3ema and 5ema in the beginning of 2021.However, there are also some negative signs, such as the declining Mac and the recent volatility.Investors should carefully consider the risks and rewards before investing in ITC stock.
Overall, the stock price data for ITC is mixed.There are some positive signs, such as the upward trend in the 3ema and 5ema in the beginning of 2022.Overall, the stock price data for ITC is bullish.The stock price has been trending upwards and the technical indicators are all pointing to further gains.

CONCLUSION
Deep in India, thereisasegmentoftheprocessingmarketthatfocusesonfoodfeed, processed natural products and vegetables, empty and partially empty products, refreshments, perspective, chicken products, different meats, and red meat products.This market is spread out using soda pops, a variety of beverages, breakfast oats, bread, rolls, chocolate retailers, a significant number of chances are likely available at any one moment.Comparing the considerable style programswap with alternative methods of increasing one's hazards in the cash-connected market sectors has shown promise in the short term.
(1) the ARIMA model and LSTM model accurately predict stock price and correlation; (2) the LSTM model outperforms ARIMA; and (3) the ensemble model of ARIMA-LSTM significantly outperforms other benchmark methods.For investors interested in China stock trading, our recommended approach offers theoretical assistance and method reference.In this paper, weint roduce a Deep Convolutional Generative Adversarial Network(DCGAN) architecture to deal with the problem of forecasting the closing price of stocks.To test the empirical performance of our proposed model we use the FTSE MIB (Financial Times Stock Exchange Milano Indice di Borsa), the benchmark stock market index for the Italian national stockexchange.Byconductingbothsingle-stepandmulti-stepforecasting,weobservethatour proposed model performs better than standard widely used tools, suggesting that Deep Learning (and in particular GANs) is a promising field for financial time series forecasting.Yee-Fan Tan (2021) Audience attention is vital in Digital Signage Advertising (DSA), as it has a significant impact on the pricing decision to advertise on those media.Various environmental factors affect the audience attention level toward advertising signage.as the environmental factors that could affect the audience attention are changing fast and are generally not considered in the current pricing solutions in a timely manner.Therefore, .However, it is difficult to determine an optimal price forecasting model for DSA with the increasing number of available time-series forecasting models in recent years.Based on the 84 research articles reviewed, the data characteristics analysis in terms of linearity, stationary, volatility, and dataset size is helpful in determining the optimal model for timeseries price forecasting.This paper has reviewed the widely used time-series forecasting models and identified the related data characteristics of each model.A framework is proposed to demonstrate the model selection process for dynamic pricing in DSA based on its data characteristics analysis, paving the way for future research of pricing solutions for DSA.RESEARCH METHODOLOGY Sample size: -3(Reliance, TC Sand ITC) Companies are taken Security products selected for NSE/BSE.(IT is Most Liquidity Industry in stock market) RESEARCH GAP Despite advancements in forecasting stock market returns, significant gaps remain.Brock et al. (1992) and Timmermann (2008) highlighted the need for comparative analyses across diverse markets.Zhang et al. (2019) introduced machine learning in forecasting, but more research is needed on the effectiveness of hybrid models.Pesaran and Timmermann (2004) focused on stable periods, leaving a gap in understanding methods during market turbulence.Clements and Hendry (2005) emphasized short-term forecasts, with long-term accuracy less explored.Bao and Yang (2020) used alternative data, but systematic evaluations in varied market conditions are lacking.

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The stock price started at 216.70 in September 2018 and reached a high of 293.10 in May2019.

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The stock price started at 882.65 in September 2021 and reached a high of 865.20 in January 2022.

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The stock price has been declining since May 2023, and is currently trading below the 3ema and 5ema.

H0 there is no significance relation between the moving averages and stockprices. (Rejected )
Overall, the stock price data for ADF FOODS is bullish.The stock price has been trending upwards and the technical indicators are all pointing to further gains.Overall, the stock price data for ADF FOODS is bearish.The stock price has been trending downwards and the technical indicators are all pointing to further losses.Overall, the stock price data for DABUR INDIA LIMITED is be arise.The stock price has been trending downwards and the technical indicators are all pointing to further losses.