behavioral forecasting

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Behavioral Forecasting MS&E 444: Final Presentation Rachit Prasad, Sudeep Tandon, Puneet Chhabra, Harshit Singh Stanford University

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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 Presentation

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Page 1: Behavioral Forecasting

Behavioral Forecasting

MS&E 444: Final Presentation

Rachit Prasad, Sudeep Tandon, Puneet Chhabra, Harshit Singh Stanford University

Page 2: Behavioral Forecasting

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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Behavioral Forecasting 3

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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Behavioral Forecasting 4

Fundamentalist Prediction

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Behavioral Forecasting 5

Chartist Prediction

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Behavioral Forecasting 6

Agents’ Predictions

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Behavioral Forecasting 7

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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Behavioral Forecasting 8

Model Prediction

Fitting Window

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Behavioral Forecasting 9

Dynamic Weight Adjustment

Fundamentalists Dominate

Chartists Dominate

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Dependence on Learning Rate

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Behavioral Forecasting 11

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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Behavioral Forecasting 12

USDJPY Exchange Rate Window Length: 15 Transaction Cost: 0 01/02/1975 – 09/26/1979

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Behavioral Forecasting 13

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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Behavioral Forecasting 15

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 !