behavioral forecasting ms&e 444: final presentation rachit prasad, sudeep tandon, puneet...

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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

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

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

Behavioral Forecasting 4

Fundamentalist Prediction

Behavioral Forecasting 5

Chartist Prediction

Behavioral Forecasting 6

Agents’ Predictions

Behavioral Forecasting 7

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 8

Model Prediction

Fitting Window

Behavioral Forecasting 9

Dynamic Weight Adjustment

Fundamentalists Dominate

Chartists Dominate

Behavioral Forecasting 10

Dependence on Learning Rate

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

Behavioral Forecasting 12

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

Behavioral Forecasting 13

Daily Returns: USDJPY

01/02/1975 – 11/15/1985

Behavioral Forecasting 14

Cumulative Profit: USDJPY

01/02/1975 – 09/26/1979

Behavioral Forecasting 15

Microsoft Stock

04/28/1986 – 09/28/1989

Behavioral Forecasting 16

Binary Model: USDJPY

09/05/2000 – 06/20/2002

Behavioral Forecasting 17

Constant Parameters: USDJPY

Behavioral Forecasting 18

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 19

Thank You !