Bridging the divide in financial market forecasting: machine learners vs. financial economists

Bridging the divide in financial market forecasting: machine learners vs. financial economists

Financial time series forecasting is a popular application of machine learning methods. Previous studies report that advanced forecasting methods predict price changes in financial markets with high accuracy and that profit can be made trading on these predictions. However, financial economists point to the informational efficiency of financial markets, which questions price predictability and ...

Financial market crisis and financial market channel

Machine learning for financial market prediction

The usage of machine learning techniques for the prediction of financial time series is investigated. Both discriminative and generative methods are considered and compared to more standard financial prediction techniques. Generative methods such as Switching Autoregressive Hidden Markov and changepoint models are found to be unsuccessful at predicting daily and minutely prices from a wide rang...

FORECASTING FINANCIAL VOLATILITY: EVIDENCE FROM CHINESE STOCK MARKET by

Volatility models and their forecasts are of interest to many types of economic agents, especially for financial risk management. Since 1982 when Engle proposed the Autoregressive Conditionally Heteroscedastic (ARCH) model, there have emerged numerous models for forecasting volatility. Given the vast number of models available, agents must decide which one to use. This paper explores a number o...

Forecasting Financial Time-Series using Artificial Market Models

We discuss the theoretical machinery involved in predicting financial market movements using an artificial market model which has been trained on real financial data. This approach to market prediction in particular, forecasting financial time-series by training a third-party or ‘black box’ game on the financial data itself – was discussed by Johnson et al. in [10] and [13] and was based on som...