Learning from noisy data and Markovian processes

We discuss more realistic models of computational learning. We extend the existing literature on the Probably Approximately Correct (PAC) framework to finite Markov chains in two directions by considering: (1) the presence of classification noise (specifically assuming that the training data has currupted labelled examples), and (2) real valued function learning. In both cases we address the key issue of determining how many training examples must be presented to the learner in the learning phase for the learning to be successful under the PAC paradigm

Financial Time Series Forecasting using Agent Based Models in Equity and FX Markets

We investigate the application of machine learning Agent Based Modelling (ABM) techniques to model and forecast various financial markets including Foreign Exchange and Equities, especially models that could reproduce the time-series properties of the financial variables. We model the economy by considering non-equilibrium economics. We adopt the features that are required for modelling non-equilibrium economics using ABMs and replicate the non-equilibrium nature of the financial markets by considering a set of bounded rational heterogeneous agents, with different strategies that are ranked according to their performance in the market. We consider markets where there are different agents interacting among themselves and forming some sort of patterns. For example, the patterns are equity prices or exchange rates. While the agents have been interacting in the artificial market, the generated patterns (price dynamics) they co-produce would match with the real financial time-series. In order to get the best fit to the real market, we need to search for the best set of artificial heterogeneous agents that represents the underlying market. Evolutionary computing techniques are used in order to search for a suitable set of agent configuration in the market. We verify the forecasting performance of the artificial markets by comparing that with the real financial market by conducting out-of-sample tests

A Simultaneous Deterministic Perturbation Actor-Critic Algorithm with an Application to Optimal Mortgage Refinancing

Wedevelopasimulation-based,two-timescale actorcritic algorithm for infinite horizon Markov decision processes with finite state and action spaces, with a discounted reward criterion. The algorithm is of the gradient ascent type and performs a search in the space of stationary randomized policies. The algorithm uses certain simultaneous deterministic perturbation stochastic approximation (SDPSA) gradient estimates for enhanced performance. We show an application of our algorithm on a problem of mortgage refinancing. Our algorithm obtains the optimal refinancing strategies in a computationally efficient manner

On the laplace transforms of the first hitting times for drawdowns and drawups of diffusion-type processes

We obtain closed-form expressions for the value of the joint Laplace transform of the running maximum and minimum of a diffusion-type process stopped at the first time at which the associated drawdown or drawup process hits a constant level before an independent exponential random time. It is assumed that the coefficients of the diffusion-type process are regular functions of the current values of its running maximum and minimum. The proof is based on the solution to the equivalent inhomogeneous ordinary differential boundary-value problem and the application of the normal-reflection conditions for the value function at the edges of the state space of the resulting three-dimensional Markov process. The result is related to the computation of probability characteristics of the take-profit and stop-loss values of a market trader during a given time period

International trade network and stock market connectedness: Evidence from eleven major economies

Depth of cross-country international trade engagement is an important source of (the strength of) stock-market connectedness, depicting how directional attributes of trade determine the magnitude of spillover of stock returns across economies. We premise and test this hypothesis for a group of eleven major economies during 2000 m1-2021 m6 using both system-wide and directional evidence. We exploit the input–output network of Bilgin and Yilmaz (2018) to construct a trade-network, and use Diebold and Yilmaz, 2009, Diebold and Yilmaz, 2012, Diebold and Yilmaz, 2014 Connectedness Index to proxy for stock-market connectedness among economies. We reveal China’s instrumental role in the trade-network and its rising influence in stock markets dominated by the US. Motivated by the fact that shocks on an economy’s imports and exports may lead to different magnitude of stock market spillover to its trade partner, we further carry out a pairwise directional level investigation. Once the directional dimensions of both the trade flows and the stock market influences are considered, we find that an economy’s stock return spillover to its trade partner is generated from its position as an importer and exporter. More importantly, being an importer is found to be a stronger source of such spillover than being an exporter

TSFDC: A Trading strategy based on forecasting directional change

Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. In this paper, we develop a contrarian trading strategy named TSFDC. TSFDC is based on a forecasting model which aims to predict the change of the direction of market’s trend under the DC context. We examine the profitability, risk and risk-adjusted return of TSFDC in the FX market using eight currency pairs. We argue that TSFDC outperforms another DC-based trading strategy

Nowcasting directional change in high frequency FX markets

Directional change (DC) is an alternative to time series in recording transactions: it only records the transactions at which price changes to the opposite direction of the current trend by a threshold specified by the observer. DC can only be confirmed in hindsight: one does not know that direction has changed until it is confirmed by a later transaction. The transaction in which the price confirms a DC is called a DC confirmation point. DC nowcasting is an attempt to recognize DC before the DC confirmation point. Accurate DC nowcasting will benefit trading. In this paper, we propose a method for DC nowcasting. This method is entirely data driven: it is based on the historical distribution of DC-related indicators. Empirical results suggest that DC nowcasting is possible, even under a naïve rule. This opens the door to a promising research direction on an important topic

Backlash algorithm: A trading strategy based on directional change

Directional Change (DC) is a new way to summarize price movements in a financial market. Unlike time series, it samples data at irregular time intervals. According to the DC concept, the data is sampled only when the magnitude of price changes is significant according to the investor. In this paper, we propose a contrarian trading strategy which is based on the DC concept. We test our trading strategy using two currency pairs; namely EUR/CHF and EUR/USD. The results show that our proposed trading strategy is consistently profitable; it produce a profit of up to 145% within seven months; whereas the buy-and-hold approach incurred a loss of –14% during the same trading period

Developing sustainable trading strategies using directional changes with high frequency data

Market prices are traditionally recorded in fixed time intervals. Directional Change is an alternative approach to summarize price movements in financial markets that is consistent with across all time scales. Unlike time series, directional change summarizes the big data in finance by focusing on the intrinsic time of the data. This captures deeper intrinsic data qualities and thus trading strategies based on directional change are more sustainable and less disruptive. In this paper, we propose four trading strategies using the concept of directional change and explore the combination with technical analysis. The trading strategies are tested using EUR/USD and GBP/USD high frequency FX market data. Empirical results show good performance of our trading strategies based on thresholds, and that combining with technical analysis brings further improvement

International trade-network and stock-market connectedness: evidence from eleven major economies

Depth of cross-country international trade engagement is an important source of (the strength of) stock-market connectedness, depicting how directional attributes of trade determine the magnitude of spillover of stock returns across economies. We premise and test this hypothesis for a group of eleven major economies during 2000m1-2021m6 using both system-wide and directional evidence. We exploit the input-output network of Bilgin and Yilmaz (2018) to construct a trade-network, and use Diebold and Yilmaz’s (2009, 2012, 2014) Connectedness Index to proxy for stock-market connectedness among economies. We reveal China’s instrumental role in the trade-network and its rising influence in stock markets dominated by the US. Motivated by the fact that shocks on an economy’s imports and exports may lead to different magnitude of stock market spillover to its trade partner, we further carry out a pairwise directional level investigation. Once the directional dimensions of both the trade flows and the stock market influences are considered, we find that an economy’s stock return spillover to its trade partner is generated from its position as an importer and exporter. More importantly, being an importer is found to be a stronger source of such spillover than being an exporter