Building an Artificial Stock Market Populated by Reinforcement-Learning Agents

In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward-looking behaviour is driven by the reinforcement learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self-regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision making, application of the Q-learning algorithm for driving individual behaviour, and rich market setup.agent-based financial modelling, artificial stock market, complex dynamical system, emergent properties, market efficiency, agent heterogeneity, reinforcement learning

Meese-Rogoff Redux: Micro-Based Exchange Rate Forecasting

This paper compares the true, ex-ante forecasting performance of a micro-based model against both a standard macro model and a random walk. In contrast to existing literature, which is focused on longer horizon forecasting, we examine forecasting over horizons from one day to one month (the one-month horizon being where micro and macro analysis begin to overlap). Over our 3-year forecasting sample, we find that the micro-based model consistently out-performs both the random walk and the macro model. Micro-based forecasts account for almost 16 per cent of the sample variance in monthly spot rate changes. These results provide a level of empirical validation as yet unattained by other models. Though our micro-based model out-performs the macro model, this does not imply that past macro analysis has overlooked key fundamentals: our structural interpretation using a fundamentals-based model shows that our findings are consistent with exchange rates being driven by standard fundamentals.Exchange rates, forecasting, Meese and Rogoff, microstructure, order flow

Customer Trading in the Foreign Exchange Market: Empirical Evidence from an Internet Trading Platform

This paper analyzes the relationship between currency price changes and their expectations. Currency price change expectations are derived with the help of different order flow measures, from the trading behavior of investors on OANDA FXTrade, which is an internet trading platform in the foreign exchange market. We investigate whether forecasts of intra-day price changes on different sampling frequencies can be improved with the information contained in the flow of our investors’ orders. Moreover, we verify several hypotheses on the trading behavior and the preference structure of our investors by investigating how past price changes affect future order flow.Customer Dataset, Order Flow, Price Changes, Foreign Exchange Market

Do Analysts Tell the Truth? Do Shareholders Listen? An Experimental Study of Analysts' Forecasts and Shareholder Reaction

This work experimentally examines forecasting and trading behavior. Subjects play the role of both analyst and shareholder over the course of experiments consisting of a series of repeated games with or absent con icts of interest. In a stylized trading setting, I test whether standard equilibrium, normative behavior, or limited strategic reasoning best predicts behavior. In the presence of con icts of interest a substantial proportion of subjects' behavior appears non-skeptical in the role of shareholder, though the same subject is deceptive in the role of analyst. Absent con icts of interest, subjects behavior in the role of shareholder is nearer a best response to the same subject's behavior as analyst. The results are consistent with limited strategic reasoning and suggest that simply disclosing con icts of interest does not evoke skepticism of forecasting, nor does the elimination of con icts of interest in itself induce honesty.Experimental finance, under-reaction, overreaction, behavior, price inertia, risk aversion

How well do the markets understand Fed policy?

Presentation to the Center for Financial Studies, Frankfurt, Germany - Nov. 30, 2000Markets ; Monetary policy

Feature-driven improvement of renewable energy forecasting and trading

M. A. Muñoz, J. M. Morales, and S. Pineda, Feature-driven Improvement of Renewable Energy Forecasting and Trading, IEEE Transactions on Power Systems, 2020.Inspired from recent insights into the common ground of machine learning, optimization and decision-making, this paper proposes an easy-to-implement, but effective procedure to enhance both the quality of renewable energy forecasts and the competitive edge of renewable energy producers in electricity markets with a dual-price settlement of imbalances. The quality and economic gains brought by the proposed procedure essentially stem from the utilization of valuable predictors (also known as features) in a data-driven newsvendor model that renders a computationally inexpensive linear program. We illustrate the proposed procedure and numerically assess its benefits on a realistic case study that considers the aggregate wind power production in the Danish DK1 bidding zone as the variable to be predicted and traded. Within this context, our procedure leverages, among others, spatial information in the form of wind power forecasts issued by transmission system operators (TSO) in surrounding bidding zones and publicly available in online platforms. We show that our method is able to improve the quality of the wind power forecast issued by the Danish TSO by several percentage points (when measured in terms of the mean absolute or the root mean square error) and to significantly reduce the balancing costs incurred by the wind power producer.European Research Council (ERC) under the EU Horizon 2020 research and innovation programme (grant agreement No. 755705) Spanish Ministry of Economy, Industry, and Competitiveness through project ENE2017-83775-P

Artificial Neural Networks in Finance Modelling

The study of Artificial Neural Networks derives from first trials to translate in mathematical models the principles of biological “processing”. An Artificial Neural Network deals with generating, in the fastest times, an implicit and predictive model of the evolution of a system. In particular, it derives from experience its ability to be able to recognize some behaviours or situations and to “suggest” how to take them into account. This work illustrates an approach to the use of Artificial Neural Networks for Financial Modelling; we aim to explore the structural differences (and implications) between one- and multi- agent and population models. In one-population models, ANNs are involved as forecasting devices with wealth-maximizing agents (in which agents make decisions so as to achieve an utility maximization following non- linear models to do forecasting), while in multi-population models agents do not follow predetermined rules, but tend to create their own behavioural rules as market data are collected. In particular, it is important to analyze diversities between one-agent and one-population models; in fact, in building one-population model it is possible to illustrate the market equilibrium endogenously, which is not possible in one-agent model where all the environmental characteristics are taken as given and beyond the control of the single agent. A particular application we aim to study is the one regarding “customer profiling”, in which (based on personal and direct relationships) the “buying” behaviour of each customer can be defined, making use of behavioural inference models such as the ones offered by Artificial Neural Networks much better than traditional statistical methodologies.Artificial Neural Network, Financial Modelling, Customer Profiling

Predicting the Effects of News Sentiments on the Stock Market

Stock market forecasting is very important in the planning of business activities. Stock price prediction has attracted many researchers in multiple disciplines including computer science, statistics, economics, finance, and operations research. Recent studies have shown that the vast amount of online information in the public domain such as Wikipedia usage pattern, news stories from the mainstream media, and social media discussions can have an observable effect on investors opinions towards financial markets. The reliability of the computational models on stock market prediction is important as it is very sensitive to the economy and can directly lead to financial loss. In this paper, we retrieved, extracted, and analyzed the effects of news sentiments on the stock market. Our main contributions include the development of a sentiment analysis dictionary for the financial sector, the development of a dictionary-based sentiment analysis model, and the evaluation of the model for gauging the effects of news sentiments on stocks for the pharmaceutical market. Using only news sentiments, we achieved a directional accuracy of 70.59% in predicting the trends in short-term stock price movement.Comment: 4 page