Evaluation of the robusticity of mutual fund performance in Ghana using Enhanced Resilient Backpropagation Neural Network (ERBPNN) and Fast Adaptive Neural Network Classifier (FANNC)

Mutual fund investment has played a very important role in the world financial markets especially in developing economies where the capital market is not very matured and tolerant of small scale investors (Song et al. 2017). For example, according to the Investment Company Institute, the total mutual fund asset globally as at the end of 2016 was in excess of $40.4 trillion. To date the largest mutual fund markets are located in United States ($18.9 trillion), Luxembourg ($3.9 trillion), Ireland ($2.2 trillion), Germany ($1.9 trillion), France ($1.9 trillion), Australia ($1.6 trillion), United Kingdom ($1.5 trillion), Japan ($1.5 trillion), China ($1.3 trillion) and Brazil ($1.1 trillion) (Chattopadhyay et al. 2018). In the United States, mutual funds play an important role in U.S. household finances. At the end of 2016, 22% of household financial assets were held in mutual funds. Their role in retirement savings was even more significant, since mutual funds accounted for roughly half of the assets in individual retirement accounts, 401(k) s and other similar retirement plans (Song et al. 2017). In total, mutual funds are large investors in stocks and bonds. Luxembourg and Ireland are the primary jurisdictions for the registration of UCITS funds. These funds may be sold throughout the European Union and in other countries that have adopted mutual recognition regimes (El Ghoul and Karoui 2017).

Quarshie (2017) reveals that the mutual fund industry in Ghana dates back to pre-colonial times with the traditional “Susu” system still practiced among rural dwellers in Ghana. In this traditional capital market setup, a group of locals (market women, hawkers, farmers, drivers, artisans, and mostly low and informal income earners etc., contributes periodic amounts of money into a savings fund. This fund is managed by a designated individual or group of individuals who in turn return the accumulated total contributions to the member at the end of a pre-determined period. The challenge of dishonest stewards increased the reconstitution of these informal groups into formalized groups such as cooperative unions.

These cooperative unions adopted the same modus operandi as the traditional “Susu” saving clubs but their operations were more formalized and offered opportunities for members to take loans in from the contribution. Further cooperative unions were also trained to invest the resources of their members through the now defunct Cooperative Bank. In the early 1990s the Government of Ghana promulgated the Security Industry Law (PNDCL-333) which has subsequently been amended in Security Industry Law (2016) to allow legal framework and government led institution to overseas and ensure transparency in the market due to its susceptibility to abuse. Since then the mutual fund industry as an alternative capital market has witnessed tremendous growth. By the end of 2010, the mutual fund industry in Ghana had become a highly regarded performing investment market in Africa in particular and developing markets as a whole. Among the notable mutual fund in Ghana with international appeals include the Data Bank EPACK (first mutual fund in Ghana). Despite its success there are uncertainties as to whether mutual funds in Ghana obtain optimal performance relative to their counterparts in United States, Luxembourg, Ireland, France, Australia, United Kingdom, Japan, China and Brazil (Xu et al. 2018).).

In less developed economies like Ghana, industry players prefer using raw returns in their annual financial reports which do not necessarily reflect superior performance as any unskilled investor can increase raw returns by undertaking highly risky investment. In other to evaluate mutual fund’s performance, there are a lot of measures that can be used to do the evaluation. Among which are the Jensen Alpha Index, Sharpe and Treynor as well as Capital Asset Pricing Model (CAPM) Ibikunle and Steffen (2017). These measures are widely used especially in terms of evaluating the risk-adjusted returns of the fund where volatility and variability can be measured. In spite of this, evaluation for mutual funds are done basically on periodic basis either in weeks, months or quarterly making it very important only for comparing past performances. In other to meet the fast changing trends dimensions and trends in market conditions, performance evaluation aerobics must update repetitively and anytime at request of the user (Eldering and Igoe 2010). The performance measures tool are adopted more often for evaluation but they normally do not provide predictive variables and as such makes it difficult to be used in forecasting superior mutual funds. To be able to deal with such situation, interested researchers have adopted and employed various approaches to get predictive variables in order to make forecasting of superior mutual funds.

One of the evaluation methods which have been the focus of a valuable development is Artificial Neural Networks (ANN) since forecasting and calculating abilities of ANN are superior to native algorithms in several angles (Omer et al., 2015). Originally, Backpropagation Neural Networks (BPNN) is an ANN model widely used in finance with a supervised neural network which can analyse continuous data (Pan et al. 2017). Enhanced resilient back propagation neural networks (ERBPNN) are emerging models better in bankruptcy classification than other statistical instruments because it gives an accurate response (Siddiqui et al. 2018). Rezaee et al. 2018) used ERBPNN to predict the bankruptcy risk of major US carriers. ERBPNN was also used by Han et al. (2018) to improve bond rating in the stock market.

Multi-layer perceptron (MLP) is a feedforward neural network with one or more layers between input and output layer. Feed-forward means that data flows in one direction from input to output layer. This type of network is trained with the back-propagation learning algorithm. MLPs are widely used for pattern classification, recognition, prediction and approximation. This was applied in a work done by Afroz et al. (2017) to predict mutual fund performance where they obtain good forecasting results in blended funds, but not for growth funds. Krauss et al. (2017) suggested a hybrid intelligent system that predicts the failure of firms based on past financial performance data by combining a rough set approach with MLP. There is a positivity of enhanced resilient backpropagation neural networks to integrate fundamental and technical analysis for financial performance prediction et al., 2018).

Aside the fact that ERBPNN is commonly applied in financial literatures, it has its own limitations thus, the cost of training is mostly high, local factors often mislead the results, and its online learning is highly impossible. Other forms of Artificial Neural Network models are designed for classification problems that helps eliminate the challenges of Backpropagation Neural Network, Example is the Self Organization Map (SOM) Pun and Shahi (2018) and Adaptive Resonance Theory (ART) (Kaffash and Marra 2017; Majhi et al. 2018) families. These types of neural model unlike the ERBPNN, can be trained quickly and be put in an unknown pattern without accurate information. However, most of these neural models are unsupervised models features which limit their application in financial fields.

Ding et al. (2018), developed a new approach called FANNC to neural networks. It is suitable in its algorithm particularly when it comes to giving fast and instant response to the continually changing financial market conditions. The method used by Mustafa et al. (2017) is based on adaptive resonance theory (ART) and field theory (FT). ART can perform online learning and work under a non-stationary world. To enable one-pass learning and perform real-time supervised learning at high speed, Coulomb potential model for electrostatic forces provided the basis for field theory approach to artificial neural network. The feedback connections function is to transfer an active signal to each successive layer within the four-layer structured neural network called FANNC to implement competition and resonance. This can be seen in illustration below (Fig. 1).

The use of FANNC integrates the concept of the attracting basin, which is represented in theory as the electric field produced by the trained instance. Every second-layer unit defines an attracting basin by a certain responsive centers and the responsive characteristics widths of the Gaussian weights connected with them. The second-layer units are normally used to internally classify inputs, whereas the third-layer units are used internally for output classification. This study is necessary in light of doubtful analytical techniques on financial sector performance in Ghana with the closure and liquidation of several leading Ghanaian institutions within the last year and the need to reclaim customer confidence in the financial sector. We contribute to the current literature by testing an ensemble of more sophisticated, novel, enhanced resilient analytical neural networks tools to evaluate the performance of mutual funds listed on the Ghana Stock Exchange to obtain robustness of inference. The inputs and outputs instances are discussed in section 2, results and training processes are then provided in section 3, section 4 discussion is based on comparing and analyzing results and finally section 5 is the conclusion of the study.

The output and input instance combined which is discussed in the later section are grouped into two parts. Four-fifth of them are used for training and one-fifth are for testing.

The idea behind the paper is to do evaluation of mutual fund performance base on it flexibility and responsiveness utilizing FANNC, after which the results is compared with those from ERBPNN based model. FANNC is a subset of neural network which is newly developed and it combines the characteristics of ART and field theory. In our test, apart from the fact that FANNC does not only require significantly lesser time in evaluating mutual fund performance than the ERBPNN approach but it also has superiority in terms of RMSE record. The results holds for both classification problems and prediction problems. Additionally, the algorithm nature of the FANNC guarantees fast processing time and it makes on-line learning much easier thus making the FANNC motive for financial presentations connecting enormous volumes of data and a repetitive updates.