Purpose : The study aims to develop insights on the progress made in agreeing upon the set of macroeconomic factors be included in the arbitrage pricing theory (APT). Theoretical Framework : The paper is brief review of relevant literature focusing on studies that have tested different macroeconomic factors for possible relationship with stock returns. Design/Metho - dology/Approach : A brief overview of the literature was performed to determine the macroeconomic factors with a positive relationship with stock returns to be used in the APT from studies on the cost of capital evaluations. Findings : The article contributes to the relevant literature by showing that there is consensus on interest rate as the macroeconomic factor that strongly explain s the relationship with stock returns. Other microeconomic factors such as inflation, exchange rates, money supply and industrial production showed strong prospects but require more tests. Research, Practical & Social Implications : The article identifies the macroeconomic factors that need further studies based on the prospects shown in the studies reviewed. Practically, the article contributes to narrowing the research gap on macroeconomic factors to be used in the APT. Originality/Value : The article adds value to the body of knowledge by showing that only interest rate has been agreed upon as the macroeconomic factor with a positive relationship with stock returns. The article also narrows the gap of the set of macroeconomic factors that need further testing thereby hastening the process leading to the use of the APT.
Organizations exist to create value for the owners of the factors of production. The resultant value from the economic activities is also deemed as return on stock. Hiransha et al. (2018) surmised that stocks also known as shares are traded on the stock market. In the process of generating value, organizations interact with the external environments and as such the bearing of macroeconomic factors on the return on stock cannot be avoided.
In a bid to evaluate the returns on stocks, scholars developed models that incorporate external factors obtaining in the market and the economy as a whole. The first such model which evaluates the return on stocks while incorporating external factors is the capital asset pricing model (CAPM) initially advanced by Sharpe in 1964 and supported by Lintner in 1965 and later by Black in 1972 (French, 2017). Due to dissatisfaction with the fact that CAPM only accounted for systematic risk factor termed as beta (Reza et al., 2018), scholars sought to introduce models capable of including multiple factors.
In 1976, a model capable of using multiple macroeconomic factors in evaluating the returns on stock aptly named the arbitrage pricing theory (APT) was advanced by one of the critics of CAPM named Stephen Ross (Elshqirat, 2019). Ever since the development of the APT, there arose a huge debate regarding the type and number of factors to be universally used in the model. Many scholars have alluded to the fact that no consensus has so far been reached on factors and number of factors to be used in the APT (Tursoy et al., 2008; Benaković & Posedel, 2010; Reza et al., 2018; Amtiran et al., 2017).
The aim of this article is to show the progress made in arriving at the set of macroeconomic factors to be used in the APT Model. This paper is a must read for researchers and students alike as it evaluates the progress made over the last 45 years and narrows the knowledge in terms of the macroeconomic factors that have so far shown strong prospects in explaining the relationship with stock returns. The article clearly pinpoints the macroeconomic factors which have been commonly agreed by researchers and are ready to be included in the APT model. The article starts with a brief historical background showing the series of events and timelines from CAPM to APT and brings to the fore some of the microeconomic factors that have been found to explain the relationship with stock returns. The article ends with a summary that shows the macroeconomic factors that have so far been commonly agreed upon by inference and those that still further testing.
A brief overview of the literature was performed to determine the macroeconomic factors with a positive relationship with stock returns to be used in the APT from studies on the cost of capital evaluations. A search for peer reviewed articles on the APT was conducted using SCOPUS and Google Scholar.
Stock valuation seems to have always fascinated scholars and as a result, several views have been expressed. There is compelling evidence to believe that earlier reasoning on stock valuation did not make famous models. For instance, Duffie (2010) attributed the general equilibrium model of security markets to the work of Arrow in 1953 and traced it back to Walras in 1877. While the ground had been laid for scholarly debate on valuations of security markets, the real interest was only seen 11 years later with the introduction of the CAPM.
The CAPM was devised by Sharpe in 1964 and was supported by Lintner in 1965 and later by Black in 1972 and subsequently, the single-factor model got the name of the SLB model after the names of the contributing authors (French, 2017). Arguably, CAPM is one of the oldest theories that explain risk analysis, stock returns and movements in prices of stocks and accounts only for systematic risk factor termed as beta (Reza et al., 2018).
Despite earning the alternative term of SLB in 1972, the reasoning contained in the CAPM model was questioned by scholars as other macroeconomic factors were found to explain stock returns other than the market beta. Since CAPM was a one-factor model, several models with multiple were later developed aimed bringing to the fore different other macroeconomic factors:
1) Fama and French (1993, as cited in Ekaputra & Sutrisno, 2020) developed the three-factor (FF3) model which included market, size and book-to-market factors as an extension to CAPM. The model was found to explain the cross-section stock returns;
2) Carhart (1997, as cited in Ahmed et al., 2018) developed four-factor (FF4) model that added momentum to FF3;
3) Fama and French (2015, as cited in Sarwar et al., 2017) developed a five-factor (FF5) model, comprised of the market, profitability, size, value, and investment factors;
4) Barillas and Shanken (2018, as cited in Ahmed et al., 2018) later crafted the six-factor (BS6) model made up of the market, size, and momentum factors of the FF5 model to which profitability, investment and the value factors were added.
In 1976, one of the renowned critic of CAPM and its assumptions, by the name of Stephen Ross introduced the APT which is an equilibrium theory viewed by all intents and purposes as an alternative to CAPM and employed many more macroeconomic factors each with its risk factor (beta) to denote the stock expected rate of return (Elshqirat, 2019). Amtiran et al. (2017) further observed that APT is capable of using a single factor, multifactor and the market portfolio model determine the relationship between macroeconomic factors and stock returns. The APT provided a strong case for ensuring that stock returns are tied to macroeconomic factors.
From the time that APT was crafted, a whole new debate arose in terms of the macroeconomic factors to be included. The reasoning for the lack of specification for the APT Model is supported by Elshqirat (2019). Scholars cannot however be possibly faulted for having gone all out to test all sorts of macroeconomic indicators in search of relevant factors. To a large extent, the excitement was due to the APT being premised on the understanding that stocks should be the main indicator of economic activity since stock markets reflect the fundamentals of the economy (Reza et al., 2018).
As a result of scholarly frenzy, several studies to establish the relationship between macroeconomic factors and stock prices were conducted in several countries including Jordan, the USA, Arabic countries, Pakistan, Nigeria, south Asia and African countries (Elshqirat, 2019). At its best, studies of the relationship between macroeconomic factors and stock returns have been around the world in every continent (
The analysis focuses on the macroeconomic factors that showed a positive causality relationship with stock returns. Of the 18 studies reviewed covering the period from 1986 and 2019, 10 macroeconomic factors showed a positive relationship with stock returns and these include interest rates, inflation, exchange rates, GDP, industrial production, CPI, money supply, reserves, market index and oil prices. The 10 macroeconomic factors that showed a positive relationship with stock returns represent a 59% score against all the variables considered in the studies reviewed in the article.
In
The results in
| Author(s) | Countries in which the study was conducted | Macroeconomic factors studied | Microeconomic factors with significant relationship with stock returns |
|---|---|---|---|
| Chen et al. (1986 as cited in Naik, 2013 ) | USA | Industrial production, inflation (anticipated and unanticipated), long- and short-term government bond yield | All the factors were found to be significant |
| (Elshqirat, 2019) | Jordan | Exports, unemployment rate, Gross domestic product (GDP), and industrial producers price index (IPPI) | None |
| Al-Abdallah & Aljarayesh (2017) | interest rate, inflation and exchange rate | Interest rate and inflation | |
| Gan et al. (2006, as cited in Reza et al., 2018 ) | New Zealand | interest rates, inflation, and exchange rates | None |
| Barakat et al. (2016, as cited in Reza et al., 2018 ) | Greece and Tunisia | CPI, exchange rate, money supply, and interest rates | CPI, exchange rate, money supply, and interest rates were positive for Greece and Tunisia. CPI was not applicable for Tunisia |
| Zulfa and Tan (2009, as cited in Reza et al., 2018 ) | Indonesia | Inflation and interest rate | Inflation |
| Reza et al. (2018) | Indonesia | Exchange rate, inflation and GDP | Exchange rate |
| Amtiran et al. (2017) | Indonesia | GDP, Interest rate, inflation rate and the exchange rate | GDP, Interest rate, exchange rate |
| Paavola (2006) | Russia | Inflation, oil prices, money supply, exchange rates, and production industry | All the factors were found to be significant |
| Tursoy, Gunsel, & Rjoub (2008) | Turkey | Oil prices, money supply, consumer price index, industrial production, import, export, gold prices, exchange rate, interest rates, economic growth, foreign reserve, unemployment rate and market pressure index | None of the factors had a significant relationship with stock returns |
| Benakovic & Posedel (2010) | Croatia | inflation, industrial production, interest rates, market indices and oil prices | market index, Interest rates, oil prices and industrial production |
| Olorunleke (2014) | Nigeria | output growth, interest rate and inflation rate | inflation rate, interest rate and real GDP |
| Rashid (2008) | Pakistan | Consumer prices, industrial production, exchange rate and the market rate of interest | All macroeconomic variables tested significant |
| Wongbampo and Sharma (2002, as cited in Olorunleke, 2014 ) | Philippines, Malaysia, Thailand, Indonesia and Singapore | interest rates | Interest rates was significant only for Malaysia and Indonesia |
| Rahman et al. (2009) | Malaysia | money supply, interest rate, exchange rate, reserves and industrial production index | All macroeconomic variables tested significant |
| Issahaku et al. (2013) | Ghana | interest rate, inflation and money supply and Foreign Direct Investment (FDI) | In the short-term, interest rate, inflation and money supply tested significant. while inflation, money supply and Foreign Direct Investment (FDI) were significant in the long run |
| Naik (2013) | India | industrial production index, inflation, money supply, short term interest rate exchange rates and stock market index | money supply and industrial production |
| Hunjra et al. (2014) | Pakistan | Interest rate, exchange rate, and GDP and inflation rate | All factors tested significant only in the long run |
| Macroeconomic Factor with positive for relationship with stock returns | Studies | Positive score of factor out of 18 studies reviewed (A) | Positive score out of studies that featured factor (B) | Weighted average score (C) |
|---|---|---|---|---|
| Interest rate | Amtiran et al. (2017); Benaković & Posedel (2010); Olorunleke (2014); R ashid, (2008); Wongbampo and Sharma (2002, as cited in Olorunleke, 2014 ); Rahman et al. (2009) ; Hunjra et al. (2014) ; Issahaku et al. (2013) ; Al-Abdallah & Aljarayesh (2017) ; Barakat et al. (2016, as cited in Reza et al., 2018 ); Chen et al. (1986 as cited in Naik, 2013 ) | 61% | 73% | 45% |
| Inflation | Chen et al. (1986 as cited in Naik, 2013 ); Al-Abdallah & Aljarayesh (2017) ; Zulfa and Tan (2009, as cited in Reza et al., 2018 ); Paavola (2006) ; Olorunleke (2014) ; Issahaku et al. (2013) ; Hunjra et al. (2014) | 39% | 58% | 23% |
| Exchange rates | Barakat et al. (2016, as cited in Reza et al., 2018 ); Reza et al. (2018); Amtiran et al. (2017); Paavola (2006); Rashid, (2008); Rahman et al. (2009); Hunjra et al. (2014) | 39% | 64% | 25% |
| GDP | Amtiran et al. (2017); Olorunleke (2014); Hunjra et al. (2014) | 17% | 75% | 13% |
| Industrial production | Chen et al. (1986 as cited in Naik, 2013 ); Paavola (2006); Benaković & Posedel (2010); Rashid (2008); Rahman et al. (2009) ; Naik (2013) | 33% | 75% | 25% |
| CPI | Barakat et al. (2016, as cited in Reza et al., 2018 ); Rashid (2008) | 11% | 67% | 7% |
| Money supply | Barakat et al. (2016, as cited in Reza et al., 2018 ); Paavola (2006) ; Rahman et al. (2009) ; Issahaku et al. (2013) ; Naik (2013) | 28% | 83% | 23% |
| Oil prices | Benaković & Posedel (2010) ; Paavola (2006) | 11% | 66% | 7% |
| Market index | Benaković & Posedel (2010) | 6% | 33% | 2% |
| Reserves | Rahman et al. (2009) | 6% | 50% | 3% |
also showed that macroeconomic factors which have been least featured in studies tend to have a higher score of positive relationship with stock returns. For instance, 25% of studies reviewed showed that GDP and industrial production were negatively related to stock returns.
Despite the assumption that macroeconomic factors and stock returns are linearly related, the evidence from the table above shows that there is no agreement on the factors to comprise the APT model. When conducting a study on the relationship between stock returns and macroeconomic variables, the most prominent limitation of the APT model that one is faced with is that the types and numbers of factors are not known upfront. The observation has made a prominent feature in literature as observed by Benaković and Posedel (2010), Reza et al. (2018), Amtiran et al. (2017) and Tursoy et al. (2008). The failure to agree on factors and the number of factors to comprise the APT model 45 years down the line while is probably supposed to show a conducive debate amongst scholars unfortunately shows how complex the global economy tend to be. While the linearity of the relationship between the assumption of the relationship between the macroeconomic variables and stock returns is not a bone of contention, it is self-evident that national economies vary.
From the weighted average results in
Macroeconomic factors such as Industrial production and exchange rates showed great positivity in relation to stock returns at 75% and 64% respectively. Despite the industrial production and exchange rates macroeconomic factors not having been tested by many studies as seen by the weighted average score of 25%, it would be interesting to see the results of the variables if multiple studies are conducted. Despite getting weighted average score of 23%, money supply and inflations scored 83% and 58% respectively.
Weighted average scores in this study were used to even out the extreme ends of the result in the context of the 18 studies reviewed. Despite the strong significant relationship between macroeconomic factors namely Industrial production, exchange rates, money supply and inflation and stock returns, the number of tests done were too few to support a conclusion. Out of the 18 studies reviewed, the macroeconomic factors were featured by less than half of the articles. The author is of the considered view that there is a need for further testing of the four macroeconomic factors in both developing and developed economies before adding them to the APT model.
This study is an endeavor to develop insights on the progress made in agreeing upon the set of macroeconomic factors to be used in APT. The brief review of literature has shown that there is a on consensus interest rate as one of macroeconomic factors with a positive relationship with stock returns. Even though it has taken 45 years of testing different macroeconomic factors to find the variables that can be used in the APT, there is still work to be done. APT is a multi-factor model which needs additional macroeconomic factors over and above the interest rate. The article thus recommends that four more macroeconomic factors namely industrial production, exchange rates, money supply and inflation that showed strong prospects be subjected to further testing across economies.
The authors declare no conflicts of interest regarding the publication of this paper.
Nyanga, C., & Qutieshat, A. (2022). Progress Made towards Consensus on Arbitrage Pricing Theory Macroeconomic Factors: A Brief Review of Literature. Open Journal of Business and Management, 10, 789-797. https://doi.org/10.4236/ojbm.2022.102044