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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
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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