Stock market prices can be defined as the nominal value of firms and industries within a market or an economy. Macroeconomic indicators are statistical indicators which are used for assessment of general state of the country’s economy during a certain period (Rogers 1998). Paul Samuelson’s quote was ironic, intended at highlighting flaws of using stock market prices to predict macro-economic variables. In this essay, I will analyse the causality and correlation between stock market prices and macro-economic variables in equity markets, over the long and short-term, and across different countries’ economies. I would also use top-down and bottom-down approach in analysing these variables, GDP, interest rates, inflation, budget deficit and employment. The Dividend Valuation Model (DVM) expresses prices as a function of dividends and cost of equity in the equation, P = (Dividend(d))/(Cost of Equity(KE)) . Stock prices tend to rise with earnings per share and although the ratios may differ according to macro-economic variables they tend to be in range. Stock market prices are more volatile and have been argued to fluctuate based on the expectations on future prices (Basu and Nag 2013).
GDP is a measure of economic growth. Rapid GDP growth shows an increasing economy with opportunities for firms to profit. An alternative measure of economic output is ‘industrial production’. Regression based results mostly find relationships between stock returns and real GDP. Chen, Roll ; Ross (1986) found that industrial production index is positively significant in explaining stock returns. Basu and Nag (2013) argue economic development and stock market growth are “strongly and positively correlated”, whilst in Greece, Hondroyiannis ; Papapetrou (2001) found a weak positive relationship.
Top-down approach shows GDP growth could result in an increase in market prices in an economy in the short-run as expected profitability could increase, but it would have no long-run relationship with stock prices as was the case in Malaysia (Mansor H Ibrahim, 1999). Using Grangers causality to investigate lead-lag relationships, Bhattacharya and Mukherjee (2001) propose industrial production leads market prices in India, whilst John Thornton (1993) suggested “share prices tended to lead real GDP and real GDP volatility tends to lead share price volatility in the UK. The reaction to volatility is based on the level of anticipation as “asset prices react sensitively to economic news, especially unanticipated news” (Roll and Ross, 1986). Bottom-up valuation process and economic theory show that a fall in stock market prices either from a decrease in dividends or a rise in cost of equity could lead to lower GDP and vice-versa. Duca G. (2007) supported this theory through the “wealth effect”. This shows a decrease in the perceived wealth of individuals when market prices fall, leading to a decrease in total consumption in the economy that further reduces total market demand and GDP growth. Also, an “unidirectional Granger-causal relationship” was found in the United States, United Kingdom and Malaysia (Mahdavi and Sohrabian, 1991., and Hon-Chung Hui, 2013.) where growth of stock price Granger-caused the growth rate of GDP, with no reverse causation observed.
Criticising Ducas’s theory, the study involved ten countries which are ‘developed market economies’, with no ’emerging market economies’ used. Also, the pro-cyclical nature between house prices and GDP leads to house prices cycles being used to predict cycles in GDP. Interestingly, there is a lack of Granger-causality between house prices and consumption which falsifies the wealth effect argument between house market prices and GDP. Therefore, a huge decline in housing market prices on the consumption component of GDP could be relatively weak. I agree stock prices commonly tend to lead real economic activity; however, certain factors may alter this result such as the business cycle. This results in the different market trends that could distort the profit expectations, cost (interest rates), and inflation expectation components of price. Also, inefficient markets which does not reflect the economic situations and market bubbles could distort the relationship between real GDP and stock price.
An alternative for investing in stock markets when rates are high would be investing in governments bonds which are fixed-income instruments. Interest rate is a conventional form of monetary policy set by the Central bank that affects the volatility of stock market prices. Interest rate and stock market prices are negatively corelated. An increase in rates, reduces the present value of future cash flow through dividends and capital gains, it increases the cost of variable-rate debt, and disincentivises investors to increase leverage. An increase in rates with dividends unchanged would increase the cost of equity of investing, as buying on margin becomes more expensive for investors, the demand for shares lowers and the prices of stock markets reduces causing a decline in stock markets. This increase in short-term rates is a contractionary monetary policy, favoured by Central banks aimed at reducing demand and reducing high market prices. Thus, a significant statistical negative relationship is found between these variables for example Abdullah & Hayworth (1993). However, Bohl et al (2003) found a positive but statistically insignificant relationship between German stock returns and short-term interest rates.
The direction of causality between stock prices and interest rate are varied. Chakradhara (2008) found a “bidirectional short-run causality” and a “unidirectional long-run causality” from interest rates to stock-prices. Abdullah & Hayworth (1993) found short and long-term interest rate Granger-cause stock return. Shiller and Beltrati (1992) showed there is a negative correlation between the change in actual real log stock prices and the change in actual long-term interest rates in the United States and United Kingdom (1989). Darrat and Dickens (1999) and Habibullah et al (2000) suggests that interest rates lead stock returns and prices.
Zhou (1996) shows that the long-term interest rates are more crucial than short-term interest rates in explaining changes in the stock markets. However, a different study in India by Prakash (1997) found no evidence of a long-run relationship between interest rate, industrial production, and M1. I believe these differing results arise from the nature of markets as India is not as developed as America and England. There is a difference in timeframe used in both studies. The business cycle could also determine the magnitude of these relationships. Interest rate being countercyclical will be more impactful during the cycles peaks and trough, also, the rate of substitution between debt and equity securities will be higher in peaks and troughs.
Inflation is the increase in the nominal value of assets, thus an increase in stock prices without any increase in real variables. Inflation occurs when there is too much money after too few goods (Siegel,2002). This increases the nominal price of stocks, and the response to an increased inflation by the Central Bank is to raise interest rates, to reduce the excess supply of money. This raise in the rates is bad for stock returns and prices. World war II the correlation between inflation and equity premiums were positive but became negative post-war. Fisher’s (1930) theory suggests a positive relationship between stock market returns and inflation. Actual inflation is positively correlated with unexpected inflation as there are unforeseen changes in the price level thus, a positive relationship between inflation and stock prices as unexpected inflation raises the firms’ equity value assuming that the firms are net debtors (Ioannidis et al., 2005). Inflation could also negatively affect the discount rate leading to a reduction in the present value. DeFina (1991) argued that rising inflation initially has a negative effect on corporate income due to immediate rising costs and slowly adjusting output prices, reducing profits and therefore, the share price. Referencing economic theory, the “Inflation Illusion” hypothesis (Modigliani and Cohn, 1979) advocates that when inflation rises investors discount expected earnings and dividends more severely by using higher discount rates. This leads to equities being undervalued when inflation is high and overvalued when inflation falls, thereby generating a negative relationship between equity returns and price inflation.
High inflation in markets are usually a leading variable to recession which is fall in economic growth. This was evident in cases of the great depression in 1930, dot-com bubble in 2000 and the sub-prime crises that kick started the 2008 recession, all cases show excess demand over existing capacity led to high inflation and a collapsing market. Of the variables studied by Hijazi (2004), “inflation has the largest negative long-term relationship to stock prices. Despite Olowe (2007) finding a causality between the two, his result contradicted Hijazi’s findings as, “inflation had a positive long-term relationship with equity prices”. Abdullah & Hayworth (1993) suggest inflation Granger-causes stock returns. This difference could be because of the structural economic differences between Pakistan and Nigeria respectively as well as the time differences of these studies and their economies. Once again, the business cycle will determine the impact of Inflation. The perception of rising stock prices in an up-swing would not be as unsettling than an increase in prices towards peaks, because rising stock prices in the peak could signal a bubble or an upcoming recession.
The budget balance shows the proportion of government spending to receipts. An increase in government deficit implies that the government spends more in the economy than it receives. As one of the macro objectives of the economy is to have a positive balance, governments aim to reduce the deficit and this in turn would affect the stock market. Governments may choose to increase taxation in the economy or reduce the proportion of spending. An increase in taxation such as corporation tax would reduce the dividend as profits are taxed by a higher percentage. Taxes could also reduce the demand for products and increase the cost of capital in the market, leading to a fall in the stock prices. A reduction in the government spending would reduce the aggregate demand for output in the economy which implies reduced demand for firms’ products leading to a fall in market prices. Governments also offer higher rates on government bonds which is a more attractive alternative for investors and this leads to the ‘crowding-out effect’ which in turn reduces the market prices further. Although the long-run effect of a deficit is negative, it does not always have a negative correlation with market prices. If the deficit resulted from an increase in capital expenditures like infrastructure rather than current expenditures such as salaries, there could be an increase in market prices. Thus, an uncertain correlation exists. This is supported by Donatas (2010) who found different results through Granger causality relations and coincidence, where state debt was a leading variable in Lithuania and a lagging variable in both Estonia and Latvia to stock their market indexes.
Using Money supply. monetary growth, due to its positive relationship with the inflation rate will adversely affect stock prices by increasing current and future inflation expectations as the quantity theory of money suggests, this will therefore negatively affect stock return. Money supply could also have a positive effect on stock prices through an increase in the amount of liquid money that is available. This could increase prices of stocks and return on equity through reducing the cost of investing as interest rates are low.
Abdullah ; Hayworth (1993) suggested a strong positive relationship in the US stock market.
Sahadevan and Raju (1995) found a positive association between stock prices and money and some evidence of causation running from money to stock prices. Nasrin.et.al. (2011) suggested a unidirectional causality exists from stock market to M1 and through bivariate error- correction a long-run causality exists from M1 to stock prices. Darrat and Mukherjee (1987) find that inflation and long-term interest rate have negative impact upon the stock prices thus a significant causal relationship between stock market returns and money supply, “implying market inefficiency in the semi-strong sense”. Whilst Abdullah ; Hayworth (1993) found money growth to Granger-cause stock prices, Kraft and Kraft (1977) suggested that both current and past money movements cannot Granger-cause stock price, therefore money supply is not useful in predicting stock prices.