Asset returns and inflation

Deep learning networks are applied to stock market analysis and prediction.A comprehensive analysis with different data representation methods is offered.Five-minute intraday data from the Korean KOSPI stock market is used.The network applied to residuals of autoregressive model improves prediction.Covariance estimation for market structure analysis is improved with the network. We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data representation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using high-frequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methodsprincipal component analysis, autoencoder, and the restricted Boltzmann machineon the networks overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive network is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction.

Deep learning networks for stock market analysis and prediction

Feature extraction from financial data is one of the most important problems in market prediction domain for which many approaches have been suggested. Among other modern tools, convolutional neural networks (CNN) have recently been applied for automatic feature selection and market prediction. However, in experiments reported so far, less attention has been paid to the correlation among different markets as a possible source of information for extracting features. In this paper, we suggest a CNN-based framework, that can be applied on a collection of data from a variety of sources, including different markets, in order to extract features for predicting the future of those markets. The suggested framework has been applied for predicting the next day’s direction of movement for the indices of S&P 500, NASDAQ, DJI, NYSE, and RUSSELL based on various sets of initial variables. The evaluations show a significant improvement in prediction’s performance compared to the state of the art baseline algorithms.

CNNpred: CNN-based stock market prediction using a diverse set of variables

A data mining procedure to forecast daily stock market return is proposed.The raw data includes 60 financial and economic features over a 10-year period.Combining ANNs with PCA gives slightly higher classification accuracy.Combining ANNs with PCA provides significantly higher risk-adjusted profits. In financial markets, it is both important and challenging to forecast the daily direction of the stock market return. Among the few studies that focus on predicting daily stock market returns, the data mining procedures utilized are either incomplete or inefficient, especially when a large amount of features are involved. This paper presents a complete and efficient data mining process to forecast the daily direction of the S&P 500 Index ETF (SPY) return based on 60 financial and economic features. Three mature dimensionality reduction techniques, including principal component analysis (PCA), fuzzy robust principal component analysis (FRPCA), and kernel-based principal component analysis (KPCA) are applied to the whole data set to simplify and rearrange the original data structure. Corresponding to different levels of the dimensionality reduction, twelve new data sets are generated from the entire cleaned data using each of the three different dimensionality reduction methods. Artificial neural networks (ANNs) are then used with the thirty-six transformed data sets for classification to forecast the daily direction of future market returns. Moreover, the three different dimensionality reduction methods are compared with respect to the natural data set. A group of hypothesis tests are then performed over the classification and simulation results to show that combining the ANNs with the PCA gives slightly higher classification accuracy than the other two combinations, and that the trading strategies guided by the comprehensive classification mining procedures based on PCA and ANNs gain significantly higher risk-adjusted profits than the comparison benchmarks, while also being slightly higher than those strategies guided by the forecasts based on the FRPCA and KPCA models.

Forecasting daily stock market return using dimensionality reduction

This paper aims to develop an innovative neural network approach to achieve better stock market predictions. Data were obtained from the live stock market for real-time and off-line analysis and results of visualizations and analytics to demonstrate Internet of Multimedia of Things for stock analysis. To study the influence of market characteristics on stock prices, traditional neural network algorithms may incorrectly predict the stock market, since the initial weight of the random selection problem can be easily prone to incorrect predictions. Based on the development of word vector in deep learning, we demonstrate the concept of “stock vector.” The input is no longer a single index or single stock index, but multi-stock high-dimensional historical data. We propose the deep long short-term memory neural network (LSTM) with embedded layer and the long short-term memory neural network with automatic encoder to predict the stock market. In these two models, we use the embedded layer and the automatic encoder, respectively, to vectorize the data, in a bid to forecast the stock via long short-term memory neural network. The experimental results show that the deep LSTM with embedded layer is better. Specifically, the accuracy of two models is 57.2 and 56.9%, respectively, for the Shanghai A-shares composite index. Furthermore, they are 52.4 and 52.5%, respectively, for individual stocks. We demonstrate research contributions in IMMT for neural network-based financial analysis.

An innovative neural network approach for stock market prediction

The main objective of this research is to forecast the daily direction of Standard & Poor's 500 (S&P 500) index using an artificial neural network (ANN). In order to select the most influential features (factors) of the proposed ANN that affect the daily direction of S&P 500 (the response), design of experiments are conducted to determine the statistically significant factors among 27 potential financial and economical variables along with a feature defined as the number of nodes of the ANN. The results of employing the proposed methodology show that the ANN that uses the most influential features is able to forecast the daily direction of S&P 500 significantly better than the traditional logit model. Furthermore, experimental results of employing the proposed ANN on the trades in a test period indicate that ANN could significantly improve the trading profit as compared with the buy-and-hold strategy.

https://link.springer.com/content/pdf/10.1186%2F2251-712X-9-1.pdf

Forecasting S&P 500 index using artificial neural networks and design of experiments

Corporate bond mutual funds and asset fire sales

This paper examines whether ETFs are a unique source of corporate bond fragility. Relative to mutual funds, ETFs cater to high-liquidity-demand investors, facilitate positive feedback strategies, and transmit outflows to corporate bonds via near proportional trading. Comparing yield spread changes of bonds from the same issuer, we show that ETFs create flow-induced pressure during the Taper Tantrum, a period of market turmoil. Redemptions used to maintain the relative price efficiency of the largest and most liquid ETFs lead to significantly higher yield spreads for 4 months before reverting. The pattern indicates ETFs amplify the effects of negative fundamental shocks.

The Unintended Consequences of Corporate Bond ETFs: Evidence from the Taper Tantrum

Short-sale constraints and stock price informativeness

Corporate bond mutual funds engage in liquidity transformation, raising concerns among academics and policymakers that correlated redemptions will destabilize the corporate bond market. However, estimating regressions that focus within issuer-quarter, I find little evidence that redemptions or resulting sell-offs push corporate bond prices below fundamental values. To reconcile my finding with contrasting findings for equity funds, I analyze both investor flows and portfolio management strategies. While bond fund investors demonstrate bank-run like behavior, bond fund managers hold a significant amount of liquid assets, allowing them to manage redemptions without excessively liquidating corporate bonds, even during the financial crisis.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2796413_code2415572.pdf?abstractid=2638547&mirid=3

Saeid Hoseinzade

Papers

Forecasting S&P 500 index using artificial neural networks and design of experiments

Corporate bond mutual funds and asset fire sales

The Unintended Consequences of Corporate Bond ETFs: Evidence from the Taper Tantrum

Short-sale constraints and stock price informativeness

Do Bond Mutual Funds Destabilize the Corporate Bond Market