Tutorial th Nov

Outline Hints for assignment 3 Score of assignment 2 (distributed in class)

Hints for assignment 3 Question 1 – Beta graph: seg5550E-4.pdf – Beta: In finance, the beta (β) of a stock or portfolio is a number describing the relation of its returns with that of the financial market as a whole. An asset with a beta of 0 means that its price is not at all correlated with the market; that asset is independent. A positive beta means that the asset generally follows the market. A negative beta shows that the asset inversely follows the market; the asset generally decreases in value if the market goes up and vice versa. Correlations are evident between companies within the same industry, or even within the same asset class (such as equities), as was demonstrated in the Wall Street crash of This correlated risk, measured by Beta, creates almost all of the risk in a diversified portfolio. The beta coefficient is a key parameter in the capital asset pricing model (CAPM). It measures the part of the asset's statistical variance that cannot be mitigated by the diversification provided by the portfolio of many risky assets, because it is correlated with the return of the other assets that are in the portfolio. Beta can be estimated for individual companies using regression analysis against a stock market index.

Hints for assignment 3

Risk adjusted return – Return/sigma Steps for Q1 – Import the data to workspace – Use the definition of beta, create a beta graph – Apply short/long term analysis on two stocks, and discuss the results with ordinary return. – Change the return to risk adjusted return, discuss the difference and other possible benefits of these two return.

Hints for assignment 3 Question 2 – Steps: – 1 state and compare the two algorithms. – 2 Specify a training sample size. – 3 perform estimation of two algorithm in your training sample. – 4 perform prediction on the out-of-sample. – 5 Compare results.

Hints for assignment 3 Question 3 – RSI: seg5550F-4.pdf, P14 – Steps – (a) base on p15, seg5550F-4.pdf, write a program to generate RSI signal (no results, just programs) – (b) state your strategy, compare it with MA rule. – (c) modify the program to include bollinger band (just program) – (d) construct 4-step neural network model, use RSI(t-4) and CW(t-4) as inputs. Discuss results and suggest improvement.

Matlab programs for RSI signal and Bollinger bands RSI signal – AU = k-day moving average of "daily" price increase (or "+" momentum) – AD = k-day moving average of "daily" price decrease (or "-" momentum) – Relative Strength (RS) = AU/AD – Relative Strength Index (RSI) = 100 -(100/1+RS) = 100*AU/(AU+AD) – Sample program: rand_rsi.m

Matlab programs for RSI signal and Bollinger bands Bollinger bands – Theory: Related with moving average Generated upper band and lower band of the base MA by assuming market volatility of positive and negative 2*sigma A channel bounded by the upper band and lower band – Matlab function : Bolling(asset, samples, alpha) - refer to page 3-64 of Financial Toolbox Manual – Sample program: rand_bol.m