behavioral forecasting
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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 - PowerPoint PPT PresentationTRANSCRIPT
Behavioral Forecasting
MS&E 444: Final Presentation
Rachit Prasad, Sudeep Tandon, Puneet Chhabra, Harshit Singh Stanford University
Behavioral Forecasting 2
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
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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
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Fundamentalist Prediction
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Chartist Prediction
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Agents’ Predictions
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Market Prediction Model
wf = #fundamentalists / #investorswc = #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)
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Model Prediction
Fitting Window
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Dynamic Weight Adjustment
Fundamentalists Dominate
Chartists Dominate
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Dependence on Learning Rate
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Estimation of Model Parameters
Model parameters (, , µ, S*) change with feedback (profits) The optimal parameters found by grid search and nonlinear optimization
Predict: Chartist & Fundamentalist
Find Prediction Errors & Profits over Training Window
Input Price Data
Minimize MSEPredict Next Period Price
Optimal Parameters
Advance by 1 day
Window Length
Training Period
k
Window Length
Training Period
k+1
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USDJPY Exchange Rate Window Length: 15 Transaction Cost: 0 01/02/1975 – 09/26/1979
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Daily Returns: USDJPY01/02/1975 – 11/15/1985
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Cumulative Profit: USDJPY 01/02/1975
– 09/26/1979
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Microsoft Stock04/28/1986 – 09/28/1989
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Binary Model: USDJPY09/05/2000 – 06/20/2002
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Constant Parameters: USDJPY
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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.
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Thank You !