Asset returns and inflation

Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%).

Predicting direction of stock price index movement using artificial neural networks and support vector machines

We predict long term stock price direction.We benchmark three ensemble methods against four single classifiers.We use five times twofold cross-validation and AUC as a performance measure.Random Forest is the top algorithm.This study is the first to make such an extensive benchmark in this domain. Stock price direction prediction is an important issue in the financial world. Even small improvements in predictive performance can be very profitable. The purpose of this paper is to benchmark ensemble methods (Random Forest, AdaBoost and Kernel Factory) against single classifier models (Neural Networks, Logistic Regression, Support Vector Machines and K-Nearest Neighbor). We gathered data from 5767 publicly listed European companies and used the area under the receiver operating characteristic curve (AUC) as a performance measure. Our predictions are one year ahead. The results indicate that Random Forest is the top algorithm followed by Support Vector Machines, Kernel Factory, AdaBoost, Neural Networks, K-Nearest Neighbors and Logistic Regression. This study contributes to literature in that it is, to the best of our knowledge, the first to make such an extensive benchmark. The results clearly suggest that novel studies in the domain of stock price direction prediction should include ensembles in their sets of algorithms. Our extensive literature review evidently indicates that this is currently not the case.

Evaluating multiple classifiers for stock price direction prediction

Stock market prediction is important and of great interest because successful prediction of stock prices may promise attractive benefits. These tasks are highly complicated and very difficult. In this paper, we investigate the predictability of stock market return with Adaptive Network-Based Fuzzy Inference System (ANFIS). The objective of this study is to determine whether an ANFIS algorithm is capable of accurately predicting stock market return. We attempt to model and predict the return on stock price index of the Istanbul Stock Exchange (ISE) with ANFIS. We use six macroeconomic variables and three indices as input variables. The experimental results reveal that the model successfully forecasts the monthly return of ISE National 100 Index with an accuracy rate of 98.3%. ANFIS provides a promising alternative for stock market prediction. ANFIS can be a useful tool for economists and practitioners dealing with the forecasting of the stock price index return.

An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange

Stock Closing Price Prediction using Machine Learning Techniques

There exists vast research articles which predict the stock market as well pricing of stock index financial instruments but most of the proposed models focus on the accurate forecasting of the levels (i.e. value) of the underlying stock index. There is a lack of studies examining the predictability of the direction/sign of stock index movement. Given the notion that a prediction with little forecast error does not necessarily translate into capital gain, this study is an attempt to predict the direction of S&P CNX NIFTY Market Index of the National Stock Exchange, one of the fastest growing financial exchanges in developing Asian countries. Random forest and Support Vector Machines (SVM) are very specific type of machine learning method, and are promising tools for the prediction of financial time series. The tested classification models, which predict direction, include linear discriminant analysis, logit, artificial neural network, random forest and SVM. Empirical experimentation suggests that the SVM outperforms the other classification methods in terms of predicting the direction of the stock market movement and random forest method outperforms neural network, discriminant analysis and logit model used in this study.

Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest

The present study, investigates the predictability of S&P CNX NIFTY Index returns using Support vector machines (SVM). The performance of the SVM model in forecasting Nifty index returns is rigorously evaluated in terms of widely used statistical metrics like mean absolute error, root mean square error, normalized mean square error, correctness of sign and direction change (Pesaran and Timmermann (1992, DA test), and equal forecast accuracy using Diebold and Mariano (1995, DM test) by comparing its performance with those of neural network, random forest regression and a linear ARIMA model. The four competiting models are also examined in terms of various trading performance and economic criteria like annualized return, Sharpe ratio, maximum drawdown, annualized volatility, average gain/loss ratio etc via a trading experiment. The findings of the study reveal that SVM model achieves greater forecasting accuracy and improves prediction quality compared to other models experimented in the study. The SVM model can be used as an alternative forecasting tool for Nifty Index returns and it will lead to better returns based on the traditional forecasting accuracy measures, such as root mean squared errors, and financial criteria, such as risk-adjusted measures of return.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID962833_code551806.pdf?abstractid=962833&mirid=2

Support Vector Machines Approach to Predict the S&P CNX NIFTY Index Returns

This study examines the dynamic causal (linear as well as nonlinear) relationship between trading volume and return and between volatility and returns We have used the vector autoregression based Granger causality framework to examine the linear causality, while the non-linear causality have been investigated using bivariate noisy Mackey–Glass model which has been used so far in only economic and commodity data sets The results of linear and non-linear causality show that trading volume Granger does not cause returns and volatility and suggests that there is unidirectional causality from returns to volume and from volatility to volume The results strongly support the noise trader model, partially support the sequential information model hypothesis, and contradict the efficient market hypothesis The evidence that volume does not influence stock returns and volatility can be incorporated by market participants in their trading strategies

Causal effect of volume on stock returns and conditional volatility in developed and emerging market

This study develops a hybrid model that combines Autoregressive Integrated Moving Average (ARIMA), Exponential GARCH (EGARCH) and Artificial Neural Network (ANN) to predict the daily returns of S&P CNX Nifty and S&P 500 indices by modifying Zhang’s (2003) approach. The performance of the hybrid ARIMA-EGARCH-ANN model is benchmarked against the ARIMA-EGARCH and ANN models. The empirical evidence provides superiority of the hybrid ARIMA-EGARCH-ANN model in terms of the traditional forecasting accuracy measures and Sign and directional change and delivers consistent results for the two time series. This endorses hybrid model robustness and provides its practical use in formulating a strategy for trading in the S&P 500 and Nifty indices.

A hybrid ARIMA-EGARCH and Artificial Neural Network model in stock market forecasting: evidence for India and the USA

This study presents the application and development of hybrid methodology that combines both ARIMA and Artificial Neural Network model to take advantage of the unique strengths of both linear and non-linear modeling to model and predict the stock market index returns. The performance of the hybrid ARIMA Neural Network model is compared with the performance of ARIMA and Neural Network model. The performance of the models are evaluated in terms of widely used statistical metrics, correctness of sign and direction change and various trading performance measures like annualized return, Sharpe ratio, maximum drawdown, annualized volatility, average gain/loss ratio, etc. via a trading strategy. The findings of the study reveal that the hybrid ARIMA Neural Network model developed is the best Forecasting model to achieve greater accuracy and yields better trading results.

Manish Kumar

Papers

Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest

Support Vector Machines Approach to Predict the S&P CNX NIFTY Index Returns

Causal effect of volume on stock returns and conditional volatility in developed and emerging market

A hybrid ARIMA-EGARCH and Artificial Neural Network model in stock market forecasting: evidence for India and the USA

Stock Index Return Forecasting and Trading Strategy Using Hybrid ARIMA-Neural Network Model