Part A: Time Series Application

Transcription

1 MBA 555, MANAGERIAL ECONOMICS Computer-Assisted-Learning (CAL): Neuro-economic nonlinear Business Modeling Base Year: AY 2014 Dr. Gordon H. Dash, Jr. Submissions must be made by Due Date: See class Audit Blog The purpose of this assignment is to introduce MBA students to the field of Big Data and the complexity of analysis associated with Big Data. Click Here to read the Wikipedia definition. In short, the assignment will introduce you to evolving world of high frequency and behavioral artificial intelligence structure for forecasting. The focus on a behavioral approach to model and forecast complex data is useful for any analyst that encounters high frequency stock market data (stock ticks), social network data, organized spam data, browser clicks, micro-blogging data and more. While this assignment focuses on how to model complex data structure, the methods covered in this assignment do not ignore more traditional time-series forecasting methods that are routinely introduced at the MBA level. The student will encounter some typical uses of various exponential smoothing models compared to the traditional OLS model used for forecasting purposes. Because it is free and easy to obtain, the assignment will rely upon daily stock market data. However, the curious MBA student is reminded to think out of the box to the extent that the data stream could be from any other functional field. The focus is to learn how to exam Big Data and not just finance data that often shows characteristics of complexity and chaotic behavior. Often, the term neuroeconomics is inserted into the discussion in order to convey behavioral, or artificial intelligence, applications to financial data (computational finance). The former traditional forecasting homework assignment focused solely on the family of exponential smoothing methods. This CAL compares the AI method, specifically a radial basis function artificial neural network (RANN), to complement the exponential smoothing family and the use of OLS. Learning Objectives: a) To introduce students to the acquisition of historical time series data through today (current date). b) To introduce students to the functionality of exponential smoothing methods when applied to time series data c) To introduce students to the functionality of nonlinear forecasting methods through the use of a learning technique a radial basis function (RBF) artificial neural network (ANN), or RANN, specifically, as cited in the literature, the K4- RANN. d) To estimate RANN weights as a nonlinear comparative metric(s) to traditional linear regression parameters Desired Outcomes: a) To forecast next day (end-of-day) stock (equity) price for a chosen security. b) To compare the forecast (dollar value), error, and reliability of the forecast across assigned methods. c) To execute the RANN method on the best model as obtained from CAL 1 (OLS assignment). You will need your final WinORS CAL 1 spreadsheet. Part A: Time Series Application 1. To perform the steps listed in this computational assignment requires that you first obtain actual stock market data to produce a one-period-ahead (next day market closing price) forecast. To that end each student is required to choose a ticker symbol to follow (IBM is n/a as it is used for teaching and demonstration purposes). a. Each student must choose a unique firm to model. Please send an to Professor Dash (ghdash@uri.edu). b. Yahoo is the source of most auto-imported capital market data used within WinORS computational functions. If you do not know the ticker symbol of your stock (or index), then go to the Quote lookup section in Yahoo Finance and perform a query. c. NOTE: For the Yahoo data service, index ticker symbols are preceded with a caret (^). 2. Open WinORS to create your data set. For your ticker import daily stock price close data from 2011 (Jan) to current date: a. In WinORS use the following menu sequence: /DM. b. On the dialog box of this menu, i. Enter Ticker symbol. ii. Double check (view) Insertion column (first time it is set for B4). iii. Set begin date to 2014 and end date to the current year (e.g., 2016) iv. For instrument choose Equities

2 MBA 555, Neuro-economic Nonlinear Business Modeling Page -2- v. For Type choose Price vi. For Price Format choose Adj Close. vii. Time Scale should be set to Daily viii. Click the UPDATE button when ready. ix. IBM stock history is imported into the spreadsheet from the Yahoo historical database. NOTE: Yahoo updates the equity database after market close and generally between 5 p.m. and 7 p.m. EST. Data may not load during this period. If that is the case, try later in the evening. 3. Following procedures that mimic those in step 2 above, import two (2) market predictor (independent) variables. How do you find an index such as the volatility index (^VIX)? See the appendix to this document. The RANN method has a mathematical requirement for at least 2 predictor variables; however, you certainly can use more than two this part is your unique design choice. 4. Click the FINISH BUTTON after all data has been entered. 5. Eliminate missing values (.). You may do this by highlighting the column (multiple column highlighting is supported) and using the menu tree: /ACE. Accept default range. Estimated values are shown in blue. NOTE: when working with big data estimating a few missing values has minimal (if any) impact on the statistical analysis. It is a necessary step as some solution tools cannot handle a missing value. Spreadsheet Operations 6. On Row 2 of the WinORS spreadsheet (the variable type row) change the single letter Variable type to D for the column that contains the actual Adj Close data for your stock ticker. All actual predictor variables should have a variable type of I. 7. Check the ending row (range) of the data. Yahoo data is not always balanced. Some data variables may be a day or more behind in reporting. Hence, it is important for you to know the last row of complete data for all variables as this is the last row for the required range setting. Suggestion: delete any unbalanced data rows at the end of the range. 8. Create period returns (change in value) for all three imported variables. a. Use menu tree: /ACR. b. You may select either Normal Period Returns or Log Difference Returns. c. Input Range 1 highlight from row 4 through end or range for all three imported price variables. d. Input Range 2 not used. e. Output: Set the top-most left vertex cell. For example, if imported data is in columns B, C and D, then choose cell E4 for the Output. f. Click OK

3 MBA 555, Neuro-economic Nonlinear Business Modeling Page -3- Part B: Stock Price Smoothing& AI Estimation/Prediction 9. Use the following two (2) exponential smoothing methods to obtain a forecast of the stock s closing daily price: a. ARRSE b. Holt 10. Auto-Produce your forecasting report. Before going on to the ANN method, use the /SR menu to produce a report in WriteORS (see instructions below). Be patient. When done do not minimize the report (let it go to the background). 11. Execute a one period-ahead RANN forecast model (see the definition of a one-period ahead stock price model as developed below). NOTE: This is now a built-in feature of the Radial Basis Function ANN. See below for discussion on the theory. a. Execute the following menu sequence: /ALR. Immediately set the range to span the three columns of data and all data rows (e.g., B4:D476). b. Forecast input box: Leave blank c. Table is not editable d. Under Patterns, set the training patterns so that about 33% of the data is used for this purpose. e. Choose a model. Either Returns Lag (1) or Volatility Lag (1) re: class lecture f. Choose a data normalization type (try Normalize Method 2 with min/max at -1 and 1). The Normalize and Standardize options tend to work best on stock period returns. g. The RBF method is Kajiji-4; h. Error minimization is GCV; and, i. The transfer Function is Gaussian j. Click OK to produce a solution. 12. Three tabs are now related to the solution: RBF Parameters; RBF Predicted; and, RBF Weights. You will work with Parameter and Predicted tabs. 13. Start your analysis by checking the forecast value for your stock see Parameter tab and the very end of the number sequence (last row). The forecast is in the blue color. 14. In your report, create a table and summarize the different forecast values. Compare the forecast to the actual adjusted closing price for the day. Add comment(s). 15. Produce a zoom-in view of one exponential smoothing series and one RBF series a. Use menu tree: /CDSF; Predictive Ability b. Choose two (e.g., Holt s and RBF) c. Click OK d. Zoom-in near the end of the series, including the forecast e. Use menu tree: /EC f. In WriteORS, the cursor is position at the end of the document (desired). Paste the Zoomed-in chart at this location. g. Space the cursor once or twice to the end of document Part C: Stock Returns Smoothing & AI Estimation/Prediction 16. Save your WinORS spreadsheet under a new name. Then: a. Hover the cursor over any tab (e.g., Foreasting) b. Right mouse click for the context sensitive menu c. Choose Clear-Sheet d. Choose the check-box for: All Tabs except Tab A e. When done only Tab A (the data tab remains). 17. On Row 2 of the WinORS spreadsheet (the variable type row) change the single letter Variable type to D for the column that contains the RETURNS of thel Adj Close data for your stock ticker. All columns with RETURNS for the predictor variables should have a variable type of I. 18. Check the ending row (range) of the data. Yahoo data is not always balanced. Some data variables may be a day or more behind in reporting. Hence, it is important for you to know the last row of complete data for all variables as this is the last row for the required range setting. Suggestion: delete any unbalanced data rows at the end of the range. 19. This analysis will focus on one (1) exponential smoothing model and one (1) RANN model where each is applied to (chaotic) returns data. Forecasting the next observation is not the primary goal for this application. 20. Solve Exponential smoothing first. Set the range for the one column with the returns of the stock ticker. Try either Holt s or ARSSE.

4 MBA 555, Neuro-economic Nonlinear Business Modeling Page Solve RANN with a range that includes the three columns with RETURNS data. a. Choose the model: General b. Set all remaining RANN parameters as described in points 11f 11i. c. Click OK 22. Produce a comparative chart: a. Use menu tree: /CDSF; Predictive Ability b. Choose the last two check-boxes (one exponential smoothing and one RBF) c. Click OK d. Zoom-in near the end of the series e. Use menu tree: /EC f. In WriteORS, the cursor is position at the end of the document (desired). Paste the Zoomed-in chart at this location. g. Space the cursor once or twice to the end of document Deliverable requirements include, but are not limited to: Company Ticker Holt's AARSE RANN BRK $17.74 $12.17 $14.25 a) A short fundamental analysis of the chosen stock ticker. Be sure to capture a recent stock price graph. Use Yahoo or any other financial portal to observe fundamental characteristics such as beta, EPS, earnings forecast, etc. Present at least 4 characteristics to support a paragraph statement on the investment value of the stock. b) Analyze the WinORS comparative predictions produced by exponential smoothing and RANN. Include the stock price predictability chart(s). Draw an opinion about which method you wold find the more reliable for the purpose of predicting stock price(s) for your ticker. Include a comparison of the actual outcome. That is, provide a short analysis, including a user formatted comparative data table that presents and summarizes the alternative forecasts and overall effectiveness. Be sure to address consistency / lack of consistency. Also, be sure to use the performance statistics to support your analysis (e.g., MSE, Direction, etc). c) Provide a policy statement that is representative of a recommendation you might present to the committee reviewing forecast reliability at your firm. d) From the comparative predictability chart produced after solving using the stock returns data, provide your statement / analysis about the overall effectiveness (or lack thereof) of the various techniques to model chaotic style data. Support your analysis by reporting and comparing either MSE, MAPE or both. NOTE: Do not submit data files. All analytical work is to be presented in either: Microsoft Word, WriteORS, or PDF (preferred format). NOTE: auto-created documents in WriteORS can be saved as RTF files and then subsequently opened by Word also, note that WriteORS can save your document as a PDF file. All submissions must be made by ghdash@usa.net Appendix A: How to Find Stock Market Indexes in Yahoo To search Yahoo Finance for predictor indexes, enter the ^ into the Get Quotes search input box. Narrow the search by entering one or more characters after ^.

5 MBA 555, Neuro-economic Nonlinear Business Modeling Page -5- Appendix B: One Period Ahead Prediction using Neural Networks To predict a one-period ahead value, you must solve a lagged model. The neural network approach requires the use of predictor (independent) variables. The dependent variable (more correctly known as the target variable in neural network terminology), is related to the independent variables. P b P b P IBM t 1 1 ^ VIX, t 2 ^ IRX, t t -or-, stated differently, the actual model to solved is P b P b P IBM t 1 ^ VIX, t 1 2 ^ IRX, t 1 t

6 MBA 555, Neuro-economic Nonlinear Business Modeling Page -6- Appendix C: Nonlinear RANN Parameter Estimation (Optional for Advanced Project Analysis) Instructions For this part of the assignment you must open the WinORS data file that contains the solution to the double-log regression (OLS) based homework (on file BNKHWK as transformed using natural logs). Tab A of the data file should have variable types across row 2 that correspond to your best model. That is, there is one D and multiple I types set within the proper data range. a. Solve the RANN using the same combination of variables (best model). i. Change variable type G to I ii. Use menu tree: /ALR. iii. On the RANN dialog box, try transformation Standardize 1. b. The three RBF tabs are inserted into the spreadsheet structure. c. On the RBF Weights tab, observe the weight (nonlinear regression parameter) for each variable. d. Repeat steps (i) to (iii) three additional times using data transformation: Standardized 2; Normalized 1; and, Normalized 2, respectively. Reporting requirements 1. Choose your best RANN model 2. Create a comparative table Example Table 1: RANN Solution with all G Variables Types Changed to I Variable Double Log Model Parameter Elasticity RANN Quasi-Elasticity Interest Rate: Federal Funds Rate Interest Rate: Swiss Short Term Interest Rate: Italian Short Term Total Nonfarm Employment (thousands) SA Jan. effect dummy variable Price of West Texas Crude Time Series FEB Seasonal D Oct Effect D Preferred for complete comparative decision-analysis

7 MBA 555, Neuro-economic Nonlinear Business Modeling Page -7- Example Table 2: RANN Solution RANN Ignores all G Variable Types Variable Double Log Model Parameter Elasticity RANN Quasi-Elasticity Interest Rate: Federal Funds Rate Interest Rate: Swiss Short Term Interest Rate: Italian Short Term Total Nonfarm Employment (thousands) SA Jan. effect dummy variable Price of West Texas Crude Time Series FEB Seasonal D Oct Effect D Explain how you judge the overall efficiency of the RANN model. That is, since this is a non-parametric analysis there are no confidence tests you can perform. Hence, what metrics are useful to judge overall model effectiveness. a. Fitness error, MAPE, R2, AIC, Schwarz b. Predictability chart 4. Write a short comparative summary to explain how the individual variable elasticity coefficients differ. Be sure to feature a complete numerical example using at least one of the variables. Last update: 23-May-2005; 25-May-2005; 27-May-2005; 07-Mar-2006; 20-Feb-2007; 28-May-2008; 04-Nov-2008; 20-May-2009; 21-May-2009; 24-May-2010; 03-June-2011; 25-Oct-2012; 30-Oct-2012; 10-June-2013; 31-Oct-2013 (BOO..!); 08-June-2016