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1. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis

2. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
INDEX
1. Aim of the Project
2. Literature Review
3. Methods
4. Analysis and Results
5. Conclusion
6. Thoughts for Further Studies
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3. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
1. Aim of the Project
Main motives of our research are to find logical base for below questions;
* Do stock prices conform the efficient market hypothesis from the aspect of
macroeconomic indicators ?
* Does change in main macroeconomic indicators effect stock prices ?
* If yes , What is the mathematical formulation of this effect ?
* Which indicators have superior effect on different BIST 100 stock index ?
* Is different Bist 100 index change resulted by changes in different macroeconomic
variables ? Otherwise, Can we generalize that all Bist 100 indexes’ changes have been
effected common macroeconomic indicators ?
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4. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
1. Aim of the Project
* Our main goal in this project to determine power, direction and timing of relationship
between macroeconomic indicators and BIST 100 index returns
* Microeconomic indicators has been excluded from our research. (company profitability,
industry profitability and attractiveness, competiton level, intellectual capital, company culture,
governmental subsidies over company and industry, entry barrier to market).
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5. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
2. Literature Review
* Fu-Lai Lin et al. (2018), “Stock and bond return relations and stock market uncertainty:
Evidence from wavelet analysis ”
* Regina Hammerschmid and Harald Lohre (2018), “Regime shifts and stock return
predictability”
* Rangan Gupta and Mampho P. Modise (2012), “Macroeconomic Variables and South African
Stock Return Predictability”
* Pholile Dladla and Christopher Malikane (2018), “Stock return predictability: Evidence from a
structural model”
* John Khumalo (2014), “Inflation And Stock Prices Interactions In South Africa”
* Wes McKinney (2017), “Time Series Analysis in Python with statsmodels”
*Mariano González et al. (2018), “Macroeconomic determinants of stock market betas”
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6. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
We examine various BIST indexes in our project because of having deep know-how about
economic climate, history, companies and making investment for years in that market.
Time Scope
July 2012 – August 2018
Dependant Variables:
BIST 100 Total
BIST 100 Service
BIST 100 Financial
BIST 100 Industrial
BIST 100 Technology
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Changes at monthly basis (%)
Minimizing black swan effect has been considered in the
process of date interval preference.

7. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
Independent Variables :
Producer Price Index
Industrial Production Index
Commercial Bank Funding Rate
Currency Basket
Gold Price (ons)
M2 Money Supply of TCB
Brent Oil (USD)
Copper (USD)
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Monthly basis changes (%)

8. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
Data:
* Database of TUIK and TCMB has been benefited.
* 74 months between July 2012 and August 2018 has been analyzed.
•Independent X variables (Macroeconomic indicators) has been analyzed from the aspect
of inter correlation.
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9. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
* Regression analysis has been run for eight original independent X variables and for each dependent Y variables
separately (BIST 100 Total, BIST 100 Service, BIST 100 Financial, etc..) without any smoothing.
BIST 100 Total Index
BIST 100 Service Index
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10. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
BIST 100 Financial Index
BIST 100 Industrial Index
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11. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
BIST 100 Technology
* According to first analysis of regression; there is no material relationship between our
analyzed eight macroeconomic indicators and various Index returns
* Because of above reason, independent variables (macroeconomic indicators) has been
analyzed and seasonalities has been removed with moving average method over 12
months
*Lag Test has been applied to analyze any effect on dependent variables by prior period’
s one.
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12. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
Lag test
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13. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
* In case of getting immaterial and unmeaning result between original index returns and seasonality
smoothed macroeconomic indicators, dependent y variables (BIST indexes) has been smoothed through
trend and seasonality by Winter model using excel solver. We used original dependent variables because
of enabling meaningful and material results. Smoothed dependent variables have been never used.
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14. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
* Seasonality smoothed monthly basis macroeconomic indicators (X) has been analyzed
through regression with original monthly index returns. Time lag effect has been
considered and investigated t, t-1, t-2,t-3,t-4 terms for relationship’ s power.
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15. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
* For each BIST 100 Index returns, Initial multiple regression models have been created
according to power of relationship to be find final multiple regression models.
( Significance Level 95 %)
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16. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
BIST 100 TOTAL
BIST 100 Change (t) = a + (b0 * Industrial Production Index Change (t-2)) + (b1 * Commercial Bank Funding
Rate Change (t-2)) + (b2 * Currency Basket Change (t)) + (b3 * M2 Money Supply Change (t)) + (b4 * Copper
Price Change (t-1))
BIST 100 SERVICE
BIST 100 Service Change (t) = a + (b0 * Industrial Production Index Change (t-3)) +
(b1 * Commercial Bank Funding Rate Change (t-1)) + (b2 * Currency Basket Change (t)) + (b3 * Brent Oil
Price Change (t-1)) + (b4 * M2 Money Supply Change (t)) + (b5 * Copper Price Change (t-1))
BIST 100 FINANCAIL
BIST 100 Financial Change (t) = a + (b0 * Industrial Production Index Change (t-3)) + (b1 * Currency Basket
Change (t)) +(b3 * Gold Price Change (t)) + (b4 * M2 Money Supply Change (t))
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17. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
BIST 100 INDUSTRIAL
BIST 100 Industrial Change (t) = a + (b0 * Industrial Production Index Change (t-3))
+ (b3 * Gold Price Change (t))
BIST 100 TECHNOLOGY
BIST 100 Technology Change (t) = a + (b0 * Producer Price Index Change (t)) + (b1 * Commercial Bank
Funding Rate Change (t-1)) + (b2 * Currency Basket Change (t)) + (b3 * M2 Money Supply Change (t)) +
(b4 * Copper Price Change (t-1))
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18. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
*At the last phase, raw data, macroeconomic indicators and index returns has been arranged
according to intial regression formulations as taking into account of time lag effect. It has been
made ready status to be find final regression formulas with iterations.
For example BIST 100 TOTAL initial regression was ;
BIST 100 Change (t) = a + (b0 * Industrial Production Index Change (t-2)) + (b1 * Commercial Bank Funding Rate Change (t-2)) +
(b2 * Currency Basket Change (t)) + (b3 * M2 Money Supply Change (t)) + (b4 * Copper Price Change (t-1))
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19. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
3. Methods
* Final regression formulas has been generated over intial regression formulas maximizing
number of macroeconomic indicators and power of relationship at 95 % signifinance level.
For example, BIST 100 Service Index Final multiple regression has been generated at 5th iteration.
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20. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
4. Analysis and Results
* At 95 % significance level, Final multiple regression has been generated for BIST 100 index
returns
BIST 100 TOTAL
BIST 100 Total Change (t) = 0,021 + (0,16 * Industrial Production Index Change (t-2)) - (0,21 * Currency Basket
Change (t)) + (-0,6 * M2 Money Supply Change (t)) + E
E: Epsilon (error term)
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Regression Statistics
Multiple R 0,578092
R Square 0,334191
Adjusted R Square 0,285768
Standard Error 0,035404
Observations 60
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%
Intercept 0,021 0,006 3,558 0,001 0,009 0,033 0,009 0,033
Industrial Production Index Change 0,016 0,006 2,487 0,016 0,003 0,029 0,003 0,029
Currency Basket Change -0,207 0,103 -2,021 0,048 -0,413 -0,002 -0,413 -0,002
M2 Money Supply Change -0,606 0,274 -2,211 0,031 -1,154 -0,057 -1,154 -0,057

21. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
4. Analysis and Results
BIST 100 SERVICE
BIST 100 Service Change (t) = 0,017 + (0,055 * Commercial Bank Funding Rate Change (t-1))
+ (-0,78 * M2 Money Supply Change (t)) + E
E: Epsilon (error term)
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Regression Statistics
Multiple R 0,485961
R Square 0,236158
Adjusted R Square 0,208878
Standard Error 0,046838
Observations 59
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%
Intercept 0,017 0,007 2,246 0,029 0,002 0,032 0,002 0,032
Commercial Bank Funding Rate Ch. 0,056 0,024 2,333 0,023 0,008 0,104 0,008 0,104
Money Supply Change -0,783 0,231 -3,389 0,001 -1,245 -0,320 -1,245 -0,320

22. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
4. Analysis and Results
BIST 100 FINANCIAL
BIST 100 Financial Change (t) = 0,016 + (0,017 * Industrial Production Index Change (t-3)) +
(0,13 * Gold Price Change (t)) + (-1,00 * M2 Money Supply Change (t)) + E
E: Epsilon (error term)
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Regression Statistics
Multiple R 0,551423
R Square 0,304067
Adjusted R Square 0,266107
Standard Error 0,054443
Observations 59
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%
Intercept 0,016 0,009 1,831 0,072 -0,002 0,034 -0,002 0,034
Industrial Production Index Change 0,017 0,010 1,773 0,082 -0,002 0,037 -0,002 0,037
Gold Price Change 0,134 0,057 2,334 0,023 0,019 0,249 0,019 0,249
M2 Money Supply Change -1,001 0,270 -3,714 0,000 -1,542 -0,461 -1,542 -0,461

23. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
4. Analysis and Results
BIST 100 INDUSTRIAL
BIST 100 Industrial Change (t) = 0,012 + (0,017 * Industrial Production Index Change (t-3)) +
(0,09* Gold Price Change (t)) + E
E: Epsilon (error term)
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Regression Statistics
Multiple R 0,349288
R Square 0,122002
Adjusted R Square 0,090645
Standard Error 0,045892
Observations 59
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%
Intercept 0,012 0,006 1,917 0,060 -0,001 0,025 -0,001 0,025
Industrial Production Index Change 0,017 0,008 2,045 0,046 0,000 0,033 0,000 0,033
Gold Price Change 0,092 0,048 1,895 0,063 -0,005 0,189 -0,005 0,189

24. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
4. Analysis and Results
BIST 100 TECHNOLOGY
BIST 100 Technology Change (t) = 0,031 + (0,07 * Commercial Bank Funding Rate Change (t-1)) +
(-0,495 * Currency Basket Change (t)) + E
E: Epsilon (error term)
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Regression Statistics
Multiple R 0,488619
R Square 0,238748
Adjusted R Square 0,212498
Standard Error 0,066909
Observations 61
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%
Intercept 0,0312 0,0092 3,3884 0,0013 0,0128 0,0496 0,0128 0,0496
Commercial Bank Funding Rate Ch. 0,0716 0,0338 2,1169 0,0386 0,0039 0,1393 0,0039 0,1393
Currency Basket Change -0,4951 0,1386 -3,5715 0,0007 -0,7726 -0,2176 -0,7726 -0,2176

25. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
5. CONCLUSION
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26. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
5. CONCLUSION
* 5 macroeconomic indicators have material and powerful effect on BIST 100 index returns at 95
% significance level.
* Producer Price Index, Brent Oil (USD) and Copper (USD) price level change has no material and
powerful effect on examined BIST 100 indexes at 95 % significance level.
* M2 Money Supply of Central Bank of Turkey , Currency Basket and Gold Price are the fastest
reactor for BIST 100 index change. That three macroeconomic indicator effects BIST 100 indexes
as soon as they have been declared.
* Effects on Industrial Production Index on BIST 100 indexes has been realized with at least two
months time lag.
* M2 Money Supply of Central Bank of Turkey and Currency Basket changes have in negative
way relationship with BIST 100 index returns.
* Industrial Production Index , Gold Price and Commercial Bank Funding Rates have positive
ralationship with BIST 100 index returns.
* Our final regressions R2 values indicates that micro analysis factors are monumental effect on
investor decision too besides of macroeconomic indicators.
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27. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
6. THOUGHTS FOR FURTHER STUDIES
* Research scope may be delimited with only one index as adding more micro and macro
indicators.
* Political forces and climate’s effects on investor will be analyzed with surveys and denominated
as seperate independent variable to put into regression.
* We are so acquantied with unreasonable index movements in Turkey, which is unable to reflect
general macroeconomic conditions, within international money transfers motivated by several
governmental subsidies and economic interruption. Because of that reason, research may be
repeated over politically more stable developing or developed country stock indexes.
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28. ITU Management Faculty, MBA, ISS 511 İnsan Kaynakları Yönetimi
Predicting Stock Returns with Macroeconomic Indicators on BIST 100
ITU Management Faculty, MBA, ISL 511 Investment Analysis
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