Tag Archives: Forecasting

Measuring Toxic Flow for Trading & Risk Management

A common theme of microstructure modeling is that trade flow is often predictive of market direction.  One concept in particular that has gained traction is flow toxicity, i.e. flow where resting orders tend to be filled more quickly than expected, while … Continue reading

Posted in Algorithmic Trading, ARMA, Direction Prediction, Econometrics, Econophysics, Forecasting, High Frequency Finance, Market Microstructure, Order Flow, Risk Management, Time Series Modeling, Toxic Flow | Tagged , , , | Comments Off

Market Microstructure Models for High Frequency Trading Strategies

This note summarizes some of the key research in the field of market microstructure and considers some of the models proposed by the researchers. Many of the ideas presented here have become widely adopted by high frequency trading firms and … Continue reading

Posted in Econophysics, Forecasting, High Frequency Finance, High Frequency Trading, Market Microstructure | Tagged , , , , | Comments Off

Forecasting Financial Markets – Part 1: Time Series Analysis

The presentation in this post covers a number of important topics in forecasting, including: Stationary processes and random walks Unit roots and autocorrelation ARMA models Seasonality Model testing Forecasting Dickey-Fuller and Phillips-Perron tests for unit roots Also included are a number … Continue reading

Posted in ARMA, Econometrics, Forecasting, Purchasing Power Parity, Time Series Modeling, Unit Roots, White Noise | Tagged , , , , , | 1 Comment

Regime-Switching & Market State Modeling

The Excel workbook referred to in this post can be downloaded here. Market state models are amongst the most useful analytical techniques that can be helpful in developing alpha-signal generators.  That term covers a great deal of ground, with ideas … Continue reading

Posted in ARMA, Econometrics, Fat Tails, Forecasting, Markov State Models, Regime Shifts | Tagged , , , | 10 Comments

Resources for Quantitative Analysts

Two of the smartest econometricians I know are Prof. Stephen Taylor of Lancaster University, and Prof. James Davidson of Exeter University. I recall spending many profitable hours in the 1980′s with Stephen’s book Modelling Financial Time Series, which I am … Continue reading

Posted in Econometrics, Forecasting, Time Series Modeling | Tagged , | Comments Off

Can Machine Learning Techniques Be Used To Predict Market Direction? The 1,000,000 Model Test.

During the 1990′s the advent of Neural Networks unleashed a torrent of research on their applications in financial markets, accompanied by some rather extravagant claims about their predicative abilities.  Sadly, much of the research proved to be sub-standard and the … Continue reading

Posted in Direction Prediction, Forecasting, Logit Regression, Machine Learning, Matlab, Modeling, Nearest Neighbor, Neural Networks, Nonlinear Classification, Nonlinear Dynamics, Random Forrests, S&P500 Index, Support Vector Machines | Tagged , , , , , , , | 5 Comments

Long Memory and Regime Shifts in Asset Volatility

This post covers quite a wide range of concepts in volatility modeling relating to long memory and regime shifts. The post discusses autocorrelation, long memory, fractional integration, black noise, white noise, Hurst Exponents, regime shift detections, Asian markets and various topics froms nonlinear dynamics. Continue reading

Posted in ARFIMA, Asian markets, Black Noise, Correlation Dimension, Correlation Integral, FIGARCH, Forecasting, Fractional Brownian Motion, Fractional Integration, Henon Attractor, Hurst Exponent, Logistic Attractor, Long Memory, Modeling, Nonlinear Dynamics, Pink Noise, Regime Shifts, Strange Attractor, Uncategorized, Volatility Modeling, White Noise | Tagged , , , , , , , , , , , , | 3 Comments

Market Timing in the S&P 500 Index Using Volatility Forecasts

To illustrate some of the possibilities of this approach, we constructed a simple market timing strategy in which a position was taken in the S&P 500 index or in 90-Day T-Bills, depending on an ex-ante forecast of positive returns from the logit regression model (and using an expanding window to estimate the drift coefficient). We assume that the position is held for 30 days and rebalanced at the end of each period. In this test we make no allowance for market impact, or transaction costs.
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Posted in Binary Options, Forecasting, Logit Regression, Market Timing, S&P500 Index, Volatility Modeling, volatility sign prediction forecasting Engle | Tagged , , , , , , , , | Comments Off