- •Contents
- •Introduction
- •Context
- •Graph 1 – Situation X da – company a’s demand dc – competitors’ demand
- •Financial Data Mining Literature
- •Picture 1 – Word ratio and stock performance visual comparison (Nagar and Hahsler 2012, pp. 15)
- •Competitors’ Effect Literature
- •Critical Analysis and Gap in Research
- •Proportion of competitors’ influence on the stock return
- •Proportion of the news’ effect on competitor(s)’ return
- •Justification
- •Industry return as control variable
- •Controlling for the market
- •Graph 3 – Agilent Technologies squared returns
- •Ratio Analysis
- •Competitors’ Influence - Regression
- •Industry return as control variable
- •Influence on Competitors – Regression
- •Regression analysis results
- •Industry return as control variable
- •Conclusion
- •Proportion of competitors’ influence on the stock return
- •Industry return as control variable
- •Proportion of the news’ effect on competitor(s)’ return
- •Possibility for practical use
- •Limitations of the research
- •Recommendations for future study
- •References
- •Appendix
Industry return as control variable
H0: there is no negative relationship between the return of company A and its competitors.
Ha: there is negative relationship between the return of company A and at least one of its competitors.
|
Competitor 1 |
Competitor 2 |
|
Competitor 1 |
Competitor 2 |
||||
|
p-value |
|
p-value |
|
p-value |
|
p-value |
||
A |
0.2620 |
0.0000 |
0.5433 |
0.0000 |
KLAC |
0.6159 |
0.0000 |
|
|
AAPL |
-0.1225 |
0.0000 |
-0.1480 |
0.0000 |
LLTC |
0.6531 |
0.0000 |
0.2636 |
0.0000 |
ADI |
0.6169 |
0.0000 |
|
|
LXK |
0.2218 |
0.0000 |
|
|
ALTR |
0.8655 |
0.0000 |
|
|
MSFT |
-0.5681 |
0.0000 |
0.0561 |
0.2091 |
AMAT |
0.5132 |
0.0000 |
|
|
MXIM |
0.5983 |
0.0000 |
0.2475 |
0.0000 |
AMD |
0.7195 |
0.0000 |
0.3132 |
0.0241 |
NCR |
0.0436 |
0.2060 |
0.3416 |
0.0000 |
BRCM |
0.3368 |
0.0000 |
0.4525 |
0.0000 |
NTAP |
0.0450 |
0.1442 |
0.6212 |
0.0000 |
CIEN |
0.5312 |
0.0000 |
|
|
QCOM |
0.2329 |
0.0000 |
|
|
CSCO |
0.1325 |
0.0000 |
0.0869 |
0.0028 |
QLGC |
0.1969 |
0.0004 |
0.2870 |
0.0000 |
EMC |
0.0196 |
0.4525 |
0.5082 |
0.0000 |
SANM |
0.4169 |
0.0000 |
|
|
GOOG |
0.0157 |
0.4281 |
0.1982 |
0.0000 |
TMO |
0.4254 |
0.0000 |
|
|
HPQ |
0.3557 |
0.0001 |
|
|
TXN |
0.4520 |
0.0000 |
|
|
IBM |
0.0631 |
0.0023 |
0.1650 |
0.0000 |
XLNX |
0.4520 |
0.0000 |
|
|
INTC |
0.1026 |
0.0000 |
0.2480 |
0.0000 |
XRX |
0.1928 |
0.0000 |
|
|
|
|
|
|
|
YHOO |
0.0445 |
0.0534 |
0.2693 |
0.0000 |
Table
8 – Results
|| Estimated
values of
and
P-value
less than 0.1 denotes 90% significance level
P-value
less than 0.05 denotes 95% significance level
P-value
less than 0.01 denotes 99% significance level
Similar situation happens even if we control for technology industry instead of the market as a whole. The only difference is that Apple showed highly significant negative relationship with its competitors and Microsoft’s opposite relationship with the first competitor became much stronger and highly significant. It however showed no more negative effect from the second competitor.