A Tensor-based eLSTM Model to Predict Stock Price Using Financial News

Stock market prediction has attracted much attention from both academia and business. Both traditional finance and behavioral finance believe that market information affects stock movements. Typically, market information consists of fundamentals and news information. To study how information shapes stock markets, common strategies are to concatenate various information into one compound vector. However, such concatenating ignores the interlinks between fundamentals and news information. In addition, the fundamental data are continuous values sampled at fixed time intervals, while news information occurred randomly. Such heterogeneity leads to miss valuable information partially or twist the feature spaces. In this article, we propose a tensor-based event-LSTM (eLSTM) to solve these two challenges. In particular, we model the market information space with tensors instead of concatenated vectors and balance the heterogeneity of different data types with event-driven mechanism in LSTM. Experiments performed on an entire year data of China Securities markets demonstrate the supreme of the proposed approach over the state-of-the-art algorithms including AZfinText, eMAQT, and TeSIA

Analysis of the Association Between Topics in Online Documents and Stock Price Movements

This paper aims at discovering the topics hidden in the newspaper articles that have an impact on movements of stock prices of the corresponding companies. Document topics are characterized by combinations of specific words in documents and are shared across a document collection. We describe the process of discovering the topics, the creation of a mapping of the topics to stock price movements, and quantifying and evaluating the results. As the method for finding and quantifying the association, we use machine learning-based classification. We achieved an accuracy of stock price movement predictions higher than 70 %. A feature selection procedure was applied to the features characterizing the topics in order to facilitate the process of assigning a label to the topic by a human expert.O

A High-Low Model of Daily Stock Price Ranges

We observe that daily highs and lows of stock prices do not diverge over time and, hence, adopt the cointegration concept and the related vector error correction model (VECM) to model the daily high, the daily low, and the associated daily range data. The in-sample results attest the importance of incorporating high-low interactions in modeling the range variable. In evaluating the out-of-sample forecast performance using both mean-squared forecast error and direction of change criteria, it is found that the VECM-based low and high forecasts offer some advantages over some alternative forecasts. The VECM-based range forecasts, on the other hand, do not always dominate –the forecast rankings depend on the choice of evaluation criterion and the variables being forecasted.daily high, daily low, VECM model, forecast performance, implied volatility

Prominent Numbers, Indices and Ratios in Exchange Rate Determination and Financial Crashes: in Economists’ Models, in the Field and in the Laboratory

The prior paper in this sequel, Pope (2009) introduced the concept of a nominalist heuristic, defined as a focus on prominent numbers, indices or ratios. In this paper the concept is used to show three things in how scientists and practitioners analyse and evaluate to decide (conclude). First, in constructing theories such as purchasing power and interest parity to predict exchange rates and to advocate floating exchange rates, economists unwittingly employ nominalist heuristics. Second, nominalist heuristics have influenced actual exchange rates through the centuries, and this finding is replicated in the laboratory. Third, nominalist heuristics are incompatible with expected utility theory which excludes the evaluation stage, and are also incompatible with prospect theory which assumes that, while the evaluation stage can involve systematic mistakes, the overall decision situation is ultra simple. It is so simple that: a) economists and psychologists can mechanically model and identify what is a mistake, and b) decision makers can maximise. However, contrary to prospect theory, in the typical complex situation, neither a) nor b) holds. Assuming that a) and b) hold has resulted in the 1988 crisis from applying the Black Scholes formulae to forward exchange rates and contributed to sequel financial crises including that of 2007-2009. What is required is a fundamentally different class of models that allow for the progressive anticipated changes in knowledge ahead faced under risk and uncertainty, namely models under the umbrella of SKAT, the Stages of Knowledge Ahead Theory. The paper’s findings support a single world currency rather than variable unpredictable exchange rates subjected to the vagaries of how prominent numbers, ratios and indices influence events via the models of scientists and practitioners.nominalism, money illusion, heuristic, unpredictability, experiment, SKAT the Stages of Knowledge Ahead Theory, prominent numbers, prominent indices, prominent ratios, transparent policy, nominal equality, historical benchmarks, complexity, decision costs, evaluation, maximisation, Black Scholes, Lehmann Brothers, sub-prime crisis, central bank swaps

Evidence and Ideology in Macroeconomics: The Case of Investment Cycles

The paper reports the principal findings of a long term research project on the description and explanation of business cycles. The research strongly confirmed the older view that business cycles have large systematic components that take the form of investment cycles. These quasi-periodic movements can be represented as low order, stochastic, dynamic processes with complex eigenvalues. Specifically, there is a fixed investment cycle of about 8 years and an inventory cycle of about 4 years. Maximum entropy spectral analysis was employed for the description of the cycles and continuous time econometrics for the explanatory models. The central explanatory mechanism is the second order accelerator, which incorporates adjustment costs both in relation to the capital stock and the rate of investment. By means of parametric resonance it was possible to show, both theoretically and empirically how cycles aggregate from the micro to the macro level. The same mathematical tool was also used to explain the international convergence of cycles. I argue that the theory of investment cycles was abandoned for ideological, not for evidential reasons. Methodological issues are also discussed

Insider Trading: Hayek, Virtual Markets, and the Dog that Did Not Bark.

This Essay briefly reexamines the great debates on the role of insider trading in the corporate system from the perspectives of efficiency of capital markets, harm to individual investors, and executive compensation. The focus is on the mystery of why trading by all kinds of insiders as well as knowledgeable outsiders was studiously ignored by the business and investment communities before the advent of insider trading regulation. It is hardly conceivable that officers, directors, and controlling shareholders would have remained totally silent in the face of widespread insider trading if they had seen the practice as being harmful to the company, to themselves, or to investors. By analogy with the famous article by Friedrich Hayek, The Use of Knowledge in Society, this Essay considers the problem of obtaining necessary information for managers of large corporate enterprises. The suggested analytical framework views the share price, sensitively impacted by informed trading, as a mechanism for timely transmission of valuable information to top managers and large shareholders. Informed trading in the stock market is also compared to “prediction” or “virtual” markets currently used by corporations and policymakers.