Predicting Stock Movements: Using Multiresolution Wavelet Reconstruction and Deep Learning in Neural Networks

Stock movement prediction is important in the financial world because investors want to observe trends in stock prices before making investment decisions. However, given the non-linear non-stationary financial time series characteristics of stock prices, this remains an extremely challenging task. A wavelet is a mathematical function used to divide a given function or continuous-time signal into different scale components. Wavelet analysis has good time-frequency local characteristics and good zooming capability for non-stationary random signals. However, the application of the wavelet theory is generally limited to a small scale. The neural networks method is a powerful tool to deal with large-scale problems. Therefore, the combination of neural networks and wavelet analysis becomes more applicable for stock behavior prediction. To rebuild the signals in multiple scales, and filter the measurement noise, a forecasting model based on a stock price time series was provided, employing multiresolution analysis (MRA). Then, the deep learning in the neural network method was used to train and test the empirical data. To explain the fundamental concepts, a conceptual analysis of similar algorithms was performed. The data set for the experiment was chosen to capture a wide range of stock movements from 1 January 2009 to 31 December 2017. Comparison analyses between the algorithms and industries were conducted to show that the method is stable and reliable. This study focused on medium-term stock predictions to predict future stock behavior over 11 days of horizons. Our test results showed a 75% hit rate, on average, for all industries, in terms of US stocks on FORTUNE Global 500. We confirmed the effectiveness of our model and method based on the findings of the empirical research. This study’s primary contribution is to demonstrate the reconstruction model of the stock time series and to perform recurrent neural networks using the deep learning method. Our findings fill an academic research gap, by demonstrating that deep learning can be used to predict stock movement.

Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model

Stock market historical information is often utilized in technical analyses for identifying and evaluating patterns that could be utilized to achieve profits in trading. Although technical analysis utilizing various measures has been proven to be helpful for forecasting and predicting price trends, its utilization in formulating trading orders and rules in an automated system is complex due to the indeterminate nature of the rules. Moreover, it is hard to define a specific combination of technical measures that identify better trading rules and points, since stocks might be affected by different external factors. Thus, it is important to incorporate investors’ sentiments in forecasting operations, considering dynamically the varying stock behavior. This paper presents a sentiment aware stock forecasting model using a Log BiLinear (LBL) model for learning short term stock market sentiment patterns, and a Recurrent Neural Network (RNN) for learning long-term stock market sentiment patterns. The Sentiment Aware Stock Price Forecasting (SASPF) model achieves a much superior performance compared to standard deep learning based stock price forecasting models.

Predictable patterns following large price changes and volume

Purpose The purpose of this paper is to examine the short horizon stock behavior following large price shocks in the Indian stock market. Design/methodology/approach The author followed the methodology developed by Pritamani and Singhal (2001) to the short horizon stock behavior following large price shocks. Multivariate regression has also been used to test the robustness of the evidenced results. Findings The abnormal return following large one-day price changes were not found to be important. However, large price one-day changes, conditioned with volume, evidenced significant reversals and momentum over the following 20-day period. Large price changes accompanied by low volume exhibited significant reversals and suggests significant economic profits. The large price changes accompanied by high volume exhibited continuations. Research limitations/implications Large price changes accompanied by low volume exhibited significant reversals and suggested significant economic profits. The large price changes with high volume exhibited continuations. The contrarian strategy of buying low-volume one-day losers and selling one-day winners produced significant short horizon economic profits in the Indian stock market directly contradicting the efficient market hypothesis and has behavioral implications. Practical implications In this paper, the author has unearthed significant simple profitable trading strategies based on reversals and continuation following large one-day price changes with potential for significant economic profits. Originality/value This paper provides a practical framework for profitable trading strategies based on reversals and continuation following large one-day price changes with a potential for significant economic profits. The analysis of short horizon stock behavior following large price shocks conditional on volume based on the chosen methodology has not been attempted so far in the Indian stock market.

A New Approach in Event Studies: Time Varied Analysis

This study proposes a modified market model of event study that takes into account the asynchronous behavior between individual stocks and the stock market by using an added Chebyshev polynomial term. The proposed model takes into account both the macro market performance and the micro individual stock behavior and is empirically tested. The empirical analysis results demonstrate that the proposed model improves the explanatory power of the model as well as the heteroskedasticity. More importantly, its performance is almost independent of the choice of the events and stocks.