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This presentation explores the concept of behavioral forecasting in financial markets, focusing on the strategies employed by fundamentalists and chartists. It delves into key assumptions such as bounded rationality and introduces predictive models for asset prices, emphasizing mean reversion and historical price extrapolation. The study analyzes the performance of these models using data from various asset classes, including the USDJPY exchange rate and Microsoft stock. Key findings indicate a hit-rate of approximately 53%, with profits generated sufficient to cover transaction costs.
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Behavioral Forecasting MS&E 444: Final Presentation Rachit Prasad, Sudeep Tandon, Puneet Chhabra, Harshit Singh Stanford University
Motivation Division of Investor Classes • Fundamentalists: Trade on belief in intrinsic value of asset • Chartists: Trade on current market trend, and use knowledge of previous movement of prices Assumptions • Bounded Rationality: Agents cannot assimilate all the information in a market, so perfect foresight may not hold • Prediction: Based on heuristic techniques Fundamentalist: Mean reversion to intrinsic value Chartist: Extrapolation of historical prices Behavioral Forecasting
Agent Prediction Model • Fundamentalists: Ef(t,t+1S) = - (St – St*) St: Asset price at time t : Mean-reversion coefficient St*: Fundamental price at time t • Chartists: Ec(t,t+1S) = a0 + b0t + Σ2i=1aisin(bit + ci) ai, bi, ci: constants found by fitting across a window of past asset prices Behavioral Forecasting
Fundamentalist Prediction Behavioral Forecasting
Chartist Prediction Behavioral Forecasting
Agents’ Predictions Behavioral Forecasting
Market Prediction Model wf = #fundamentalists / #investors wc = #chartists / #investors wf = exp(Pf)/ [exp(Pf) + exp(Pc)] Pf: Risk-adjusted profitability (over training period) : Learning rate parameter Pf = ∑Pf - µσf [ µ: Risk aversion parameter σf: Volatility of profits E(t,t+1S) = wf Ef(t,t+1S) + wcEc(t,t+1S) Behavioral Forecasting
Model Prediction Fitting Window Behavioral Forecasting
Dynamic Weight Adjustment Fundamentalists Dominate Chartists Dominate Behavioral Forecasting
Dependence on Learning Rate Behavioral Forecasting
Input Price Data Find Prediction Errors & Profits over Training Window Predict: Chartist & Fundamentalist Advance by 1 day Optimal Parameters Minimize MSE Predict Next Period Price Window Length Training Period k Window Length Training Period k+1 Estimation of Model Parameters • Model parameters (, , µ, S*) change with feedback (profits) • The optimal parameters found by grid search and nonlinear optimization Behavioral Forecasting
USDJPY Exchange Rate • Window Length: 15 • Transaction Cost: 0 01/02/1975 – 09/26/1979 Behavioral Forecasting
Daily Returns: USDJPY 01/02/1975 – 11/15/1985 Behavioral Forecasting
Cumulative Profit: USDJPY 01/02/1975 – 09/26/1979 Behavioral Forecasting
Microsoft Stock 04/28/1986 – 09/28/1989 Behavioral Forecasting
Binary Model: USDJPY 09/05/2000 – 06/20/2002 Behavioral Forecasting
Constant Parameters: USDJPY Behavioral Forecasting
Conclusions • Hit-Rate of about 53% is observed across asset classes. • Profits generated are sufficient to overcome transaction costs. • In addition to the base model, various strategies were attempted. The binary model showed good promise. Behavioral Forecasting
Thank You ! Behavioral Forecasting
