Deep Learning for Forecasting Stock Returns in the Cross-Section

Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition, has attracted attention in the machine learning field. This paper implements deep learning to predict one-month-ahead stock returns in the cross-section in the Japanese stock market and investigates the performance of the method. Our results show that deep neural networks generally outperform shallow neural networks, and the best networks also outperform representative machine learning models. These results indicate that deep learning shows promise as a skillful machine learning method to predict stock returns in the cross-section.Comment: 12 pages, 2 figures, 8 tables, accepted at PAKDD 201

Effect on the demand and stock returns: cross-sectional of Big Data and time-series analysis

For reducing the degree of uncertainty caused by constant change in the environment, large, medium or small, private or public organizations must support their decisions in something more than experience or intuition; they must be supported by the development of accurate and reliable forecasts in order to meet the needs in the organization planning tasks. This case study presents a growing company dedicated to the storage of perishable products and incorporates time series forecasting techniques to estimate the volume of storage to foresee the requirements of additional facilities, personnel and materials needed for product mobility

Deep Neural Trading: Comparative Study With Feed Forward, Recurrent and Autoencoder Networks

Algorithmic trading approaches based on news or social network posts claim to outperform classical methods that use only price time series and other economics values. However combining financial time series with news or posts, requires daily huge amount of relevant text which are impracticable to gather in real time, even because the online sources of news and social networks no longer allow unconditional massive download of data. These difficulties have renewed the interest in simpler methods based on financial time series. This work presents a wide experimental comparisons of the performance of 7 trading protocols applied to 27 component stocks of the Dow Jones Industrial Average (DJIA). The buy/sell trading actions are driven by the stock value predictions performed with 3 types of neural network architectures: feed forward, recurrent and autoencoder. Each architecture types in turn has been experimented with different sizes and hyperparameters over all the multivariate time series. The combinations of trading protocols with variants of the 3 neural network types have been in turn applied to time series, varying the input variables from 4 to 17 and the training period from 8 to 16 years while the test period from 1 to 2 years

Učení intervalově ohodnocených fuzzy kognitivních map algoritmem PSO pro predikci abnormálních akciových výnosů

Stock return prediction is considered a challenging task in financial domain. The existence of inherent noise and volatility in daily stock price returns requires a highly complex prediction system. Generalizations of fuzzy systems have shown promising results for this task owing to their ability to handle strong uncertainty in dynamic financial markets. Moreover, financial variables are usually in difficult to interpret causal relationships. To overcome these problems, here we propose an interval-valued fuzzy cognitive map with PSO algorithm learning. This system is suitable for modelling complex nonlinear problems through causal reasoning. As the inputs of the system, we combine causally connected financial indicators and linguistic variables extracted from management discussion in annual reports. Here we show that the proposed method is effective for predicting abnormal stock return. In addition, we demonstrate that this method outperforms fuzzy cognitive maps and adaptive neuro-fuzzy rule-based systems with PSO learning.Predikce výnosů akcií je v oblasti financí považována za náročnou úlohu. Existence inherentního šumu a kolísání denních výnosů cen akcií vyžaduje velmi komplexní predikční systém. Generalizace fuzzy systémů ukazují slibné výsledky vzhledem k jejich schopnosti modelovat silnou nejistotu na dynamických finančních trzích. Finanční proměnné jsou navíc obvykle v obtížně interpretovatelných kauzálních vztazích. Abychom překonali tyto problémy, navrhujeme zde intervalovou fuzzy kognitivní mapu s učením pomocí PSO algoritmu. Tento systém je vhodný pro modelování komplexních nelineárních problémů pomocí kauzálního usuzování. Jako vstupy systému spojujeme kauzálně propojené finanční ukazatele a jazykové proměnné, které jsou získávány z diskuse managementu ve výročních zprávách. Ukazujeme, že navrhovaná metoda je účinná pro predikci abnormálního výnosu akcií. Navíc prokazujeme, že tato metoda překonává fuzzy kognitivní mapy a adaptivní systémy založené na neuro-fuzzy pravidlech s PSO učením