







































 
 

14 
© 2022 by the authors; licensee Eastern Centre of Science and Education, USA 

 

Asian Business Research Journal 
Vol. 7, 14-20, 2022 
ISSN: 2576-6759 
DOI: 10.55220/25766759.v7i1.119 
© 2022 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
Are Stock Prices Aligned with Investors’ Expectations? Evidence from Financial 
Sector 

 
Victor Bahhouth1   

Rebecca Gonzalez2   

William Stewart Thomas3   
  

( Corresponding Author) 
 

1,2,3University of North Carolina, Pembroke, USA. 
1Email: victor.bahhouth@uncp.edu Tel: 9105216172 
2Email: Rebecca.gonzalez@uncp.edu Tel: 9105216853 
3Email: stewart.thomas@uncp.edu Tel: 9105216859 

 
Abstract 

Stock prices change as news become available about businesses; the change in price process is a 
reflection of financial markets’ expectations about firms’ performance. Consequently both, 
financial measures & fundamental measures change as a result. Researchers explored how well the 
financial measures explain firms’ performance and future stock price movements. The purpose of 
this study is to explore if the volatility of financial measures are better predictors of stock price 
movements – an empirical evidence from the financial sector during 2008 market correction. 
Study develops a two-step scenario is developed; 1- The first step examines the use of the financial 
measures as leading measures to project stock price movement during 1998 and 2007 period. 2- 
The second step examines the co-movement of financial measures volatility with stock prices 
during the same period. Study shows evidence that the co-movement between Price/ Book 
volatility and that of stock price during 2008 market correction is significant and consistent 
across the six main financial industries. 

 
Keywords: Volatility, Risk, Financial measures, Correlation, Market corrections, Stock price, Expectations. 

JEL Classification: C12; C21; G01; G12. 

 
1. Introduction 

The purpose of the study is to explore if financial measures co-movement are good indicators in predicting 
stock price movement during financial market crash periods. Market crash, flash crash, or market correction are all 
a product of systematic risk as they result in a significant drop in value of the stock market. Many studies 
challenged the argument that financial system key driver is the systemic risk. De Bandt and Hartmann (2000) 
argued that the functioning of financial system is adversely affected as a result financial crisis. Schwarcz (2009) 
explained a financial market crash event as a leading event that severely shock an economic sector and create 
simultaneous reactions with severe consequences on financial markets and institutions.  

Internet is playing a key role in shaping the stock market. It allows investors to access information about firms 
and stock market for free or at a very low cost. Information is wide in scope; it ranges from trading activities to key 
highlights about firms’ overall performance. They are expressed in terms of financial measures, which are firms’ 
unique characteristics. The purpose of this paper is to examine if financial measures volatility better explains stock 
prices volatility during financial market crash period. The study is made of five parts, which are: 1) a literature 
review; 2) research methodology; 3) data analysis; 4) limitations; & 5) conclusions and recommendations.  
 

2. Literature Review 
Indicators are frequently used to help understand changes in financial markets as well as patterns in economic 

phenomena. Private and public entities often gather and publish socio and economic indicators that help us 
understand labor markets, productivity levels, price fluctuations, and buyer behavior. While indicators can help 
predict future market movements and behaviors, their accuracy may not always be exact. When applied to the 
world of financial instruments, debt and equity market indicators can provide valuable information about how 
markets will behave. 

On the other hand, researchers argue that stock prices are driven by market reaction to news as a result stock 
prices move up with good news and move down with bad news Nettles (2003). Nettles (2003) debated that 
investors tend to invest stocks with projected high returns, which are not reflected when analyzing their firms’ 
financials.  Lei, Noussair, and Plott (2001) argued that daily trader play a key role in setting the direction of stock 
price movement; they added, rational arbitragers offset daily trader role by trading against them. In a study (Wen-
Chen & Ku-Jun, 2005), showed evidence that daily traders have significant effect on stock market. They concluded 
that daily traders drive stock prices to move away from the projected ones, which cause extreme deviations 
between the price and assets value.   

mailto:victor.bahhouth@uncp.edu
mailto:Rebecca.gonzalez@uncp.edu
mailto:stewart.thomas@uncp.edu
https://www.doi.org/10.55220/25766759.v7i1.119
https://orcid.org/0000-0002-9377-0893
https://orcid.org/0000-0003-2668-524X
https://orcid.org/0000-0001-9716-3559


Asian Business Research Journal, 2022, 7: 14-20 

15 
© 2022 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

In a study, Woida (2016) showed evidence that stock price swings are severely affect financial markets and 
occurring at a faster pace Figure 1. As a result, investors’ wealth is adversely affected with the decline in their 
investment values. For example, the market crash of 1987 created losses that exceeded 20% for most investors. 
However, this crash did not result in a recession (Baigent & Massaro, 2005). The market downturn that occurred in 
2000 caused the destruction of approximately $8 trillion worth of investor assets (DeGrace, 2011). The 2000 crash 
impacted the entire economy and did not discriminate amongst industries. The most impactful crash in recent 
history occurred in 2008. The impact of the financial crisis resulted in a loss of more than half of equity market 
values and had significant economic repercussions in global economies.  

Market crashes, their causes and repercussions have captured the attention of practitioners and researchers for 
decades. Studies have provided differing explanations for how these events start. Sornette (2004) found that 
irrational and overly optimistic investor expectations were the primary causes of market crashes. Investors relied 
on perception more than on fundamentals, and their buying and investment decisions reflected the disconnect. 
Investors favored firms that assured exceptional returns without necessarily having the financial fundamentals to 
honor their guarantee.  
 

 
Figure 1. Duration of complete Bull-Bear Cycles from 1871 through 2015 vs No. of Occurrences. 
Note: Woida (2016). 

 
Baigent and Massaro (2005) studied the 1987 crash and concluded that the use of “portfolio insurance” as a 

hedging instrument by large institutional investors was a likely culprit. They also found an increased use of 
derivative securities in the first three-quarters of 1987 led to market cap inflations. Ofek and Richardson (2003) 
studied the market crash in 2000 and found that a large differential existed between market prices and their 
fundamental intrinsic values prior to the 2000 market crash. Similarly, Zuckerman and Rao (2004) also studied the 
2000 crash and found that investments in technology stocks during the previous decade were likely a contributing 
factor given that brokers and dealers were unable to understand the consequences associated with the volatility 
seen in Internet stocks. 

One cannot study market corrections without considering volatility and its impact on subsequent market 
behaviors. Fridson (2011) determines that cultural differences impact the volatility evidenced in financial securities. 
His study looks at volatility differences in debt and equity securities. The author concludes that volatility is to be 
expected even without the abnormal economic conditions witnessed in 2008. While typical explanations such as 
exposure to new information and overreactions to new information may continue to impact asset volatility, the 
author posits that variations in volatility across geographic regions can be attributed to cultural differences 
between market participants. In fact, Fridson recommends incorporating anthropological studies and assessments 
to better understand investor behavior. 

Campbell, Koedijk, and Kofman (2002) note that while correlations exist across international returns during 
bear markets, studies that demonstrate this may be exposed to estimation bias. To eliminate concerns associated 
with such a bias, the authors model correlation as a time dependent variable since volatility varies across time. 
Their findings demonstrate a significant correlation in global returns during bear markets, even after controlling 
for the bias present in some previous studies. 

Chue, Wang, and Xu (2015) focus on style investing when analyzing return correlations. Style investing is a 
process whereby investments are made based on a style or asset category as opposed to investment selection on an 
individual asset basis. Funds are rotated amongst the different styles or categories (i.e., large cap, small cap, 
emerging market, etc.) based on the style’s market performance.  The authors constructed market portfolios, 
factoring in size, value, and momentum (as determined by cumulative six-month returns). They found that 
investment momentum is more adversely affected by severe market crashes than portfolio size and value. 

Jacquier and Marcus (2001) note that correlation relationships change often, and this change is further 
augmented during periods of high volatility. In fact, they find that much of the variation in correlation structures is 
a direct result of changes in market volatility.  Certainly, business cycles also play a role in influencing volatility. 
Erb, Harvey, and Viskanta (1994) declare that asset allocation decisions are largely influenced by correlations of 
equity returns. When studying correlations, researchers should keep in mind that these are non-static and studying 
how cross-country correlations evolve provides better insight into why we see these changes in the first place. The 
authors find that correlations between countries are impacted by the business cycles the respective countries are 



Asian Business Research Journal, 2022, 7: 14-20 

16 
© 2022 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

experiencing. There appear to be higher degrees of correlation during declining economic periods than during 
expansions. Page and Panariello (2018) draw similar conclusions. They find that higher degrees of correlation 
during recessionary periods and crashes is evident in equity securities, equity markets, equity industries, debt 
markets and currencies.  

While researchers have reached differing conclusions as to why market crashes occur, they have largely agreed 
on the use of financial measures as tools for explaining stock market phenomena. Fama and French (1992) found 
that the relationship between asset returns and size helped explain increasing risk-associated returns not otherwise 
explained in the asset-pricing model. The same could be said for price-to-book ratio and prior return measures. 
Aras and Yilmaz (2008) focused on the use of price-earnings ratios, market-to-book ratios, and dividend yields to 
help explain emerging market returns. Ang and Bekaert (2007) used price-earnings ratios to help understand 
dividend growth rates, and Lamont (1998) found the same measure was helpful in predicting excess returns. 
Lewellen (2004) reached similar conclusions regarding financial ratios in general.  

Although the reasons behind why market crashes occur vary, their increasing frequency and dire consequences 
require researchers learn more about how equity volatility and price movements work during these periods. Study 
sheds a light on the volatility of financial measures and their potential use as predictor of equity risk during market 
corrections; it eliminates industry risk by exploring an evidence from financial sector. An improved understanding 
of this relationship will help investors make better transaction decisions.  

The following research problems are addressed: 1) Are market measures key indicators of stock price 
movement during market corrections? 2) Does the volatility of market measures capture the price movement 
during market corrections?  
 

3. Research Methodology 
The research methodology of the study includes five sections, which are 1- Variables and measurements, 2- 

Sample and data collection, 3- Research instruments, 4- Data analysis, and 5- Conclusions.  
 

3.1. Variables and Measurements 
Study employs eighteen financial measures, which are divided into two groups; 1- fundamental  measures; and 

2- market measures.   
In addition, it sub-divide the financial sectors into six major industries. The following is a list of measures that 

are included in each group:  
 

3.1.1. Fundamental Measures 
Profit Margin; Return on equity; Earnings Before Interest, Taxes, Depreciation and Amortization; 

Earnings/Retention; Return on Assets; Debt to Earnings Before Interest and Taxes; Free Cash Flows+ Dividends 
/ Debt; Total Assets Turnover; Financial Leverage; Free Cash flow + Interest / Debt; Debt Service Coverage; and 
Z score.  
 

3.1.2. Market Measures 
Beta; Cumulative Change in Price; Return on Investment; Common Stock Ranking; Price Earnings; and Price 

Book Value.  
 

3.1.3. Financial Sector: 
The finance sector is made of the following industries:  
Finance Companies; Banks; Thrift Companies; Insurance; Real Estate Investment Trusts; and Dealers, Brokers 

and Investment Banks.  
 

3.2. Sample and Data Collection 
Data is made of financial measures of all public firms for the 10-year period ended by 12/31/2008 taken from 

Market Place – S&P Global.  Sample is made of 9,503 firms but few firms remained in the study due to missing 
information. 

 

3.3. Research Instrument  
Study employs two-stage research model process: 1st stage: a- Co-movement of the financial variables and 2008 

stock price volatility is measured Equation 1; b-  5% level of significance is used to test validity; c- then, a T test is 
used to examine if correlation coefficient is significantly different from zero Equation 2. 2nd stage:  a- compute 
annual financial measures volatility; b- measure financial measures’ volatility  co-movement with 2008 stock price 
volatility Equation 1; and c- test validity of co-movement of stock price volatility and financial measures volatility 
Equation 2.  
 

𝑟 =   
𝑛(∑𝑥𝑦)−(∑𝑥)(∑𝑦)

√[𝑛∑𝑥2−(∑𝑥)2][𝑛∑𝑦2−(∑𝑦)2] 
                                                                                      (1) 

t stat = (r – ƿ) / [(1-r2) / (n – 2)]0.5                                                (2) 
 

4. Data Analysis 

The process of analyzing data of financial-sector six industries is applied in two stages. Data output of banks 
industry (significant results) is reflected in Table 1. At a 5% level of significance, the coefficient of correlation of 
one financial measure volatility with that of the stock price showed a significant result; it is Price / Book value (r = 
27%, P-value 0%). Stage 2: measures the significance of the coefficient of correlation of market measures with stock 
price volatility using alpha  of 5%; four measures showed significant results. These are A- fundamental measures: 1- 
Debt/Earnings Before Interest Taxes Depreciation & amortization (r = -11%, P-value 1%), 2- Earnings Before 



Asian Business Research Journal, 2022, 7: 14-20 

17 
© 2022 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

Interest Taxes Depreciation & amortization / Assets (r = -23%, P-value 0%), and 3- Total assets turn over (r = -
23%, P-value 5%). B- market measures: 1- Price/Book Value (r = 33%, P-value = 0%).   

 
Table 1. Banks industry. 

Financial Measure 
Financial Measure Volatility Financial Measure 

N Correlation Significance N Correlation Significance 

 Price /Book Value  231 27% 0% 573 33% 0% 
 Debt / EBITDA     547 -11% 1% 
 EBITDA / Assets     548 -23% 0% 
 Total Asset Turn Over     578 -8% 5% 

 
Data output of finance companies’ industry (significant results) is reflected in Table 2. At a 5% level of 

significance, the coefficient of correlation of five financial measures volatility with that of the stock price showed a 
significant result: these are A- fundamental measures: 1- Return on Assets (r = 62%, P-value 0%); 2- Earnings 
before Interest, Taxes & Amortization / Assets (r = -32%, p-value 3%);  3- Free CFL+ Dividends / Debt (r = 46%, 
P-value 1%), and 4- Free CFL + Interest Expense/Debt (r = -59%, P-value = 0%). B- market measures:  Price 
/Book Value (r = 62.54%, P-value = 0.0%). Stage 2: Measuring the coefficient of correlation of financial measures 
with stock price volatility (Alpha 5%), six financial measures showed significant results; these are A- fundamental 
measures: 1-  Return on Assets ( r = 36%, P-value 0%), 2-  Return on Equity ( r = 32%, P-value 1%), 3-  Total 
Assets Turn Over ( r = 27%, P-value 2%), market measures: 1- Price /Book Value (r = 31%, P-value = 1%), 2- Beta 
(r= -11%, P-value = 0%), 3- Return on Investment (r = 34%, P-value 2%).  
 

Table 2. Finance companies industry. 

Financial Measure Financial Measure Volatility Financial Measure 

N Correlation Significance N Correlation Significance 

 Price /Book Value  38 79% 0% 83 31% 1% 
 Beta     886 -11% 0% 
 Return on Assets  48 62% 0% 82 36% 0% 
 Return on Equity     79 32% 1% 
 Return on Investment      80 34% 2% 
 EBITDA / Assets  44 -32% 3%    
 Total Asset Turn Over     82 27% 2% 
 Free CFL +Dividends/Debt  28 46% 1%    
 Free CFL + Interest 
Exp/Debt  

28 -59% 0%    

 
Data output of finance Insurance industry (significant results) is reflected in Table 3. At a 5% level of 

significance, the coefficient of correlation of six financial measures volatility with that of the stock price showed a 
significant result: these are A- fundamental measures: Return on Equity (r = 22.65%, P-value 1.58%), 2- Earnings 
Before Interest Taxes Depreciation & Amortization / Assets (r = 28.04%, P-value 0.52%). B- market measures: 
Price /Earnings (r = -26.02, P-value = 1.22%), 2- Price /Book Value (r = 62.54%, P-value = 0.0%), 3- Return on 
Investment (r = 25.15%, P-value 0.67%), 4- Common Stock Ranking (r = 29.74%, P-value = 1.31%). Stage 2: 
Measuring the coefficient of correlation of financial measures and stock price volatility (Alpha 5%); five financial 
measures showed significant results, and these are A- fundamental measures: 1- Return on Assets (r = 22%, P-
value 1%), 2- Return on Equity (r = 22%, P-value 0%). B- market measures: Price /Earnings (r = -17%, P-value = 
4%), 2- Price/Book Value (r = 64%, P-value = 0%), 3- ROI (r = 19%, P-value 2%).  
 

Table 3. Insurance industry. 

Financial Measure Financial Measure Volatility Financial Measure 

N Correlation Significance N Correlation Significance 

 Price /Earnings  92 -26.02% 1.22% 144 -17% 4% 
 Price /Book Value  94 62.54% 0.00% 155 64% 0% 
 Return on Assets    160 22% 1% 
 Return on Equity  113 22.65% 1.58% 160 22% 0% 
 Return on Investment   115 25.15% 0.67% 160 19% 2% 
EBITDA / Assets 98 28.04% 0.52%    
Common Stk. Ranking 69 29.74% 1.31%    

 
Data output of real estate investment trusts industry (significant results) is reflected in Table 4. At a 5% level 

of significance, the coefficient of correlation of six financial measures volatility with that of the stock price showed a 
significant result: these are A- fundamental measures: 1- Return on Assets (r = 32.13%, P-value 0.03%); 2- Return 
on Equity (r = 19.64%, P-value 3.46%); 3- Tangible Financial Leverage (r = 21.86%, P-value 0.45%). B- market 
measures: 1- Price/Book Value (r = 26.30%, P-value = 0.40%); 2- Return on Investment (r = -19.58%, P-value 
2.93%); 3- Z Score ( r = 100%, P-value 0%). Stage 2: Measuring the coefficient of correlation of financial measures  
and stock price volatility (Alpha 5%), nine financial measures showed significant results, these are A- fundamental 
measures: 1- Return on Assets (r = 31.20%, P-value 0%), 2- ROE (r = 23.40%, P-value 0.30%); 3- Earnings Before 
Interest Taxes Depreciation & Amortization / Assets (r = 35.40%, P-value 2.50%); 4- Total Asset Turn Over (r = 
14.70%, P-value 5.0%); 5- Tangible Financial Leverage (r = 25.10, P-value 0.10%); 6- Free CFL +Dividend/Debt (r 
= 20.40%, P-value 1.0%); 7- Z Score (r = 81.70, P-value 4.70%). B- market measures: 1- Price/Book Value (r = 
17.30%, P-value = 2.30%); and 2- Return on Investment (r = 20.10%, P-value 0.80%).  



Asian Business Research Journal, 2022, 7: 14-20 

18 
© 2022 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

Table 4. Real estate investment trusts industry. 

Financial Measure 
Financial Measure Volatility Financial Measure 

N Correlation Significance N Correlation Significance 

 Price/Book Value  118 26.30% 0.40% 172 17.30% 2.30% 
 Return on Assets  124 32.13% 0.03% 173 31.20% 0.00% 
 Return on Equity  116 19.64% 3.46% 163 23.40% 0.30% 
 Return on Investment    124 -19.58% 2.93% 173 20.10% 0.80% 
 EBITDA / Assets    40 35.40% 2.50% 
Total Asset Turn Over    173 14.70% 5% 
Tang Financial Leverage 167 21.86% 0.45% 167 25.10% 0.10% 
Free CFL +Dividend/Debt    160 20.40% 1.00% 
 Z Score 2 100.00% 0.00% 6 81.70% 4.70% 

 
Data output of dealers, brokers, & investment banks industry (significant results) is reflected in Table 5. At a 

5% level of significance, the coefficient of correlation of one financial measure volatility with that of the stock price 
showed a significant result: it is a market measure:  Common stock ranking (r = 70.34%, P-value 0.16%). Stage 2: 
Measuring the coefficient of correlation of financial measures and 2008 stock price volatility (Alpha 5%): None of 
the financial measures showed significant results.  
 

Table 5. Dealers, brokers, and investment banks industry. 

Financial Measure 
Financial Measure Volatility Financial Measure 

N Correlation Significance N Correlation Significance 

Common Stock Ranking 17 70.34% 0.16%    

 
Data output of thrift industry (significant results) is reflected in Table 6. At a 5% level of significance, the 

coefficient of correlation of six financial measure volatility with that of the stock price showed significant result; 
these are A- fundamental measures: 1- Return on assets (r = 27.67%, P-value 0.29%); 2- Earnings Before Interest 
Taxes Interest Depreciation & Amortization / Assets (r = 32.87%, P-value 0.05%); 3- Free CFL +Dividend/Debt 
(r = -58.48%, P-value 3.58%). B- market measures: 1- Price/Book Value (r = 78.46%, P-value = 0.00%); 2- Beta (r = 
-16.61%, P-value 3.29%); 3- Cumulative price change (r = 32.57%, P-value 0.01%). Stage 2: Measuring the 
coefficient of correlation of financial measures and 2008 stock price volatility (Alpha 5%): Nine financial measures 
showed significant results. these are: fundamental measures: 1- Earning / Retention (r = 48.40%, P-value 4.90%); 
2- Return on Assets (r = 25.40%, P-value 0%); 3- Return on Equity (r = 30.50%, P-value 0.0%); 4- Debt / Earnings 
Before Interest Taxes Depreciation & Amortization (r = 20.60%, P-value 0.60%); 5- Earnings Before Interest 
Taxes Depreciation & Amortization / Assets (r = 32.20%, P-value 0.00). B- market measures: 1- Price/Book Value 
(r = 59.10%, P-value = 0.00%); 2- Beta (r = -28.10%, P-value 0.00%); 3- Return on Investment (r = 27.50%, P-value 
0.00%); 4- Cumulative price change (r = 32.60%, P-value 0.00%).  
 

Table 6. Thrift industry. 

Financial Measure 
Financial Measure Volatility Financial Measure 

N Correlation Significance N Correlation Significance 

 Price/Book Value  95 78.46% 0.00% 199 59.10% 0.00% 
 Beta  165 -16.61% 3.29% 202 -28.10% 0.00% 

   17 48.40% 4.90% 
  Return on Assets  114 27.67% 0.29% 203 25.40% 0.00% 

   202 30.50% 0.00% 
   162 27.50% 0.00% 

Cumulative price change  138 32.57% 0.01% 138 32.60% 0.00% 
Debt / EBITDA    175 20.60% 0.60% 
EBITDA / Assets 108 32.87% 0.05% 178 32.20% 0.00% 
Free CFL+Dividends /Debt 13 -58.48% 3.58%    

 

5. Limitations of the Study 
Limitations of the study are made of the following:  
1- Study is based on a small sample because of missing data. 2- The study is based on one market incident 

(adjustment). 3- The study is based on one economic sector. 
 

6. Conclusions and Recommendations 
Study showed that the correlation coefficient of financial measures’ volatilities with that of stock price volatility 

is significant. The following is a summary of observations:  
In banking sector Table 7, correlation coefficient of Price/Book Value volatility with that of stock price 

volatility is significantly.   
 

Table 7. Banking sector industry. 

Financial Measure 
Financial Measure’s Volatility 

N Correlation Significance 
Price /Book Value 23 27% 0% 

 



Asian Business Research Journal, 2022, 7: 14-20 

19 
© 2022 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

In finance companies’ industry Table 8, the correlation coefficient of Price /Book Value; Return on Assets; and 
Earnings Before Interest Taxes Depreciation & Amortization/Assets volatilities are significantly correlated with 
the stock price’s volatility.  

 
Table 8. Finance companies industry. 

Financial Measure 
Financial Measure’s Volatility 

N Correlation Significance 

 Price /Book Value  38 79% 0% 
 Return on assets  48 62% 0% 
 EBITDA / Assets  44 -32% 3% 

 
In the insurance industry Table 9, correlation coefficient of Price/Earnings, Price /Book Value, Return on 

Equity, and Return on Investment volatilities are significantly correlated with the stock price volatility.  
 

Table 9. Insurance industry. 

Financial Measure 
Financial Measure’s Volatility 

N Correlation Significance 

 Price/Earnings  92 -26.02% 1.22% 
 Price/Book Value  94 62.54% 0.00% 
 Return on Equity  113 22.65% 1.58% 
 Return on Investment    115 25.15% 0.67% 

 
In the real estate investment trust industry Table 10 the correlation coefficient of Price/Book Value, Return on 

Assets, Return on Equity, Return on Investment, Tangible Financial Leverage, and Z score volatilities are 
significantly correlated with stock price volatility.  
 

Table 10. Real estate investment trust industry. 

Financial Measure 
Financial Measure’s Volatility 

N Correlation Significance 

 P/BV  118 26.30% 0.40% 
 ROA  124 32.13% 0.03% 
 ROE  116 19.64% 3.46% 
 Return on Investment   124 -19.58% 2.93% 
Tang Financial Leverage 167 21.86% 0.45% 
 Z Score 2 100.00% 0.00% 

 
In thrift industry Table 11, the correlation coefficient of Price/Book Value, Beta, Return on assets, Cumulative 

Price Change, Earnings Before Interest Taxes Depreciation & Amortization / assets, and Free CFL 
+Dividends/Debt volatilities are significantly correlated with the stock price volatility.  

 
Table 11. Thrift industry. 

Financial Measure 
Financial Measure’s Volatility 

N Correlation Significance 

 Price/Book Value  95 78.46% 0.00% 
 Beta  165 -16.61% 3.29% 
 Return on assets  114 27.67% 0.29% 
Cumulative price change  138 32.57% 0.01% 

EBITDA / Assets 108 32.87% 0.05% 
Free CFL +Dividends/Debt 13 -58.48% 3.58% 

 
The measure with the highest consistency across the financial sector is the volatility of Price/Book ratio; it 

exhibited significant effect across five industries. It is worthy to note that Beta, a measure of systematic risk, 
showed significant effect (strong negative) only in one sector, which is thrift industry. In addition, none of the 
financial measures showed any effect in brokers, dealers, and investment bank sectors.  

It is recommended to build on this study by exploring the effect of financial measures volatility on other 
economic sectors, using different time frames, and accounting for other variables such as firm size to address the 
reliability of the results. In addition, it is important to address the failure of Beta to explain stock price movements 
during market crash period. Finally, it is vital to address a key question; what are the real forces that are driving 
financial markets?  

 

References 
Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? The Review of Financial Studies, 20(3), 651-707. 
Aras, G., & Yilmaz, M. K. (2008). Price-earnings ratio, dividend yield, and market-to-book ratio to predict return on stock market: Evidence 

from the emerging markets. Journal of Global Business and Technology, 4(1), 18-30. 
Baigent, G., & Massaro, G. V. (2005). Derivatives and the 1987 market crash. Management Research News Patrington, 28(1), 94-105.Available 

at: https://doi.org/10.1108/01409170510784742. 
Campbell, R., Koedijk, K., & Kofman, P. (2002). Increased correlation in bear markets. Financial Analysts Journal, 58(1), 87-94.Available at: 

https://doi.org/10.2469/faj.v58.n1.2512. 
Chue, T. K., Wang, Y., & Xu, J. (2015). The crash risks of style investing: Can they be internationally diversified? Financial Analysts Journal, 

71(3), 34-46.Available at: https://doi.org/10.2469/faj.v71.n3.7. 



Asian Business Research Journal, 2022, 7: 14-20 

20 
© 2022 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

De Bandt, O., & Hartmann, P. (2000). Systemic risk: A survey: CEPR Discussion Paper No. 2634. Retrieved from: 
https://ssrn.com/abstract=258066. 

DeGrace, T. (2011). The DOTCOM bubble burst that caused the 2000 stock market crash. Retrieved from: 
http://www.stockpickssystem.com/2000-stock-market-crash/. 

Erb, C. B., Harvey, C. R., & Viskanta, T. E. (1994). Forecasting international equity correlations. Financial Analysts Journal, 50(6), 32-
45.Available at: https://doi.org/10.2469/faj.v50.n6.32. 

Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. the Journal of Finance, 47(2), 427-465. 
Fridson, M. S. (2011). Another clue to volatility. Financial Analysts Journal, 67(3), 16-22.Available at: https://doi.org/10.2469/faj.v67.n3.3. 
Jacquier, E., & Marcus, A. J. (2001). Asset allocation models and market volatility. Financial Analysts Journal, 57(2), 16-30.Available at: 

https://doi.org/10.2469/faj.v57.n2.2430. 
Lamont, O. (1998). Earnings and expected returns. the Journal of Finance, 53(5), 1563-1587.Available at: https://doi.org/10.1111/0022-

1082.00065. 
Lei, V., Noussair, C. N., & Plott, C. R. (2001). Nonspeculative bubbles in experimental asset markets: Lack of common knowledge of 

rationality vs. actual irrationality. Econometrica, 69(4), 831-859.Available at: https://doi.org/10.1111/1468-0262.00222. 
Lewellen, J. (2004). Predicting returns with financial ratios. Journal of Financial Economics, 74(2), 209-235.Available at: 

https://doi.org/10.1016/j.jfineco.2002.11.002. 
Nettles, M. (2003). Investing during turbulent times. Black enterprise. Retrieved fom: https://www.blackenterprise.com/investing-during-

turbulent-times/. 
Ofek, E., & Richardson, M. (2003). Dotcom mania: The rise and fall of internet stock prices. the Journal of Finance, 58(3), 1113-1137. 
Page, S., & Panariello, R. A. (2018). When diversification fails. Financial Analysts Journal, 74(3), 19-32.Available at: 

https://doi.org/10.2469/faj.v74.n3.3. 
Schwarcz, S. L. (2009). Keynote address: Understanding the subprime financial crisis. South Carolina Law Review, 60(3), 549-572. 
Sornette, D. (2004). A complex system view of why stock markets crash. New Thesis, 1(1), 5-18. 
Wen-Chen, L., & Ku-Jun, L. (2005). A review of the effects of investor sentiment on financial markets: Implications for investors. 

International Journal of Management, 22(4), 708-715. 
Woida, J. (2016). About every how many years will there be a stock market crash? Retrieved from: https://www.quora.com/About-every-

how-many-years-will-there-be-a-stock-market-crash. 
Zuckerman, E. W., & Rao, H. (2004). Shrewd, crude or simply deluded? Comovement and the internet stock phenomenon. Industrial and 

Corporate Change, 13(1), 171-212.Available at: https://doi.org/10.1093/icc/13.1.171. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Citation: Victor Bahhouth --- Rebecca Gonzalez --- William 
Stewart Thomas (2022). Are Stock Prices Aligned with Investors’ 
Expectations? Evidence from Financial Sector. Asian Business 
Research Journal, 7: 14-20. 
History:  
Received: 25 January 2022 
Revised:   4 March 2022 
Accepted:  18 March 2022 
Published: 6 April 2022 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Eastern Centre of Science and Education 
 

Funding: This study received no specific financial support.    
Competing Interests: The authors declare that they have no competing 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study; that no vital features of the 
study have been omitted; and that any discrepancies from the study as planned 
have been explained. 
Ethical: This study followed all ethical practices during writing. 
 

Eastern Centre of Science and Education is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use 
of the content. Any queries should be directed to the corresponding author of the article. 

 

 

http://www.stockpickssystem.com/2000-stock-market-crash/
http://www.blackenterprise.com/investing-during-turbulent-times/
http://www.blackenterprise.com/investing-during-turbulent-times/
http://www.quora.com/About-every-how-many-years-will-there-be-a-stock-market-crash
http://www.quora.com/About-every-how-many-years-will-there-be-a-stock-market-crash
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/

