Evaluating investment decisions based on the business cycle: A South African sector approach

Figures & data

Figure 1. Economic cycle and relative stock performance

Source: Stovall (1995).

Table 1. Indices of economic indicators

Leading	Coincident	Lagging
Average weekly hours of production, manufacturing Average weekly initial claims for unemployment insurance Manufactures’ new orders, consumer goods and materials Fraction of companies reporting slower deliveries Manufacturers’ new orders, nondefense capital goods Change in sensitive materials prices Share prices, 500 ordinary shares Money supply (M2) Interest rate spread, 10-year Treasury bonds less federal funds Index of consumer expectations	Employees on non-agricultural payrolls Personal income less transfer payments Industrial production Manufacturing and trade sales	Average duration of unemployment Inventories to sales ratio, manufacturing, and trade Change in index of labour cost per unit of output Average prime rate Commercial and industrial loans Consumer instalment credit outstanding to personal income ratio Change in consumer price index to services

Source: Hertzberg and Beckman (1989), Bodie et al. (2012), and The Conference Board (2016).

Table 2. The economic cycle and relative stock performance groups

Cycle phase	Characteristics	Sectors that perform well in this phase	Examples
Expansion: Early Bull phase	Inflation low but increasing Interest rates low but increasing High unused capacity Inventory low	Consumer cyclicals: Manufacturers of consumer products that react to the changes in disposable income Consumer credit: Companies that are tied to the housing industry Energy: Companies that produce energy-related products	Auto manufacturers, retailers, advertising, apparel Savings and loans, regional banks Coal, oil
Expansion: Middle Bull phase	Moderate inflation Moderate interest rates Moderate unused capacity Moderate inventory	Basic materials: Businesses manufacturing materials (not machinery) to produce finished goods Technology: Businesses manufacturing high-tech products for consumers and businesses	Chemicals, metals, plastics, paper, wood Semiconductors, computer hardware, software and services, communication equipment
Expansion: Late Bull phase	High inflation High interest rates Low unused capacity High inventory	Capital goods: Businesses manufacturing machinery used to produce finished goods Financials: Companies tied to loans that are in demand due to economic expansion Transportation: Businesses that transport passengers and goods	Equipment and machinery manufacturers Corporate and institutional bankers Airlines, trucking, railroads
Recession	Decreasing inflation Decreasing interest rates Increasing unused capacity Decreasing inventory	Consumer staples: Manufactures of basic consumer products that are purchased at largely the same level through all economic cycles Utilities: Regulated companies providing products and services such as electricity	Food, drugs, cosmetics, tobacco, liquor Electricity, gas, water
Independent of economic cycles	Varied economic circumstances	Growth Industries and businesses in the early stage of a life cycle: Expanding quickly and not subject to economic cycles	Biotechnology

Source: Thorpe (2003:10).

Table 3. List of expected best performing industries across business cycle stages

Period of expansion			Period of recession
Early expansion—Stage 1	Middle expansion—Stage 2	Late expansion—Stage 3	Early recession—Stage 4	Late recession—Stage 5
Technology	Basic materials	Agriculture	Gas and electrical utilities	Consumer cyclical
Computer software	Precious metals	Beer and liquor	Telecoms	Apparel
Measuring and control equipment	Chemicals	Candy and soda		Cars and trucks
Computers	Steel works	Food products		Business supplies
Electronic equipment	Mining	Healthcare		Construction & materials
		Medical equipment		Entertainment
Transportation	Capital goods	Pharmaceutical		Printing/publishing
General	Fabricated products	Tobacco products		Recreation
Shipping containers	Defence			Restaurants
	Machinery	Energy		Retail
	Ships and railroad equipment	Coal		Rubber and plastic
	Aircraft	Petroleum and natural gas		Textile
	Electrical equipment			Wholesale
				Financials
				Banking/insurance
				Real estate/trading

Source: Stovall (1995).

Figure 2. Empirical analysis flowchart

Source: Authors’ calculations.

Figure 3. De-trended ALSI returns using first differences

Source: Authors’ calculations.

Figure 4. Periodogram of transformed monthly GDP return data

Source: Authors’ calculations.

Figure 5. Periodogram of transformed quarterly ALSI return data

Source: Authors’ calculations.

Figure 6. Identification of business cycle and market cycle turning point dates

Source: Authors’ calculations.

Table 4. Top two performing industries compared with theory

Phase	Theory	Actual
		1st cycle	2nd cycle	3rd cycle
Early bull	Financials Transportation	Technology Consumer services	Financials Consumer services	Telecommunications Basic materials
Middle bull	Technology Capital goods	Healthcare Telecommunications	Technology Consumer goods	Healthcare Technology
Late bull	Basic industry Energy Precious metals	Telecommunications Basic materials	Telecommunications Basic materials	Telecommunications technology
Early bear	Cons non-cyclicals Healthcare	Healthcare Consumer goods	Healthcare Consumer services	Telecommunications Basic materials
Middle bear	Utilities	Basic materials Healthcare	Financials Consumer services	Telecommunications Healthcare
Late bear	Financials	Telecommunications Consumer services	Technology Financials

Source: Stovall (1995) and authors’ calculations.

Table 5. Optimal portfolio returns for different sectors and phases

		Phase	Basic materials		Consumer goods		Consumer services		Financials
			μ	σ	μ	σ	μ	σ	μ	σ
Dec-96	Feb-98	Early Bull	−2.5%	36.0%	22.4%	17.2%	86.1%	20.6%	72.2%	22.3%
Feb-98	Apr-99	Middle Bull	41.8%	21.7%	2.5%	19.5%	12.9%	20.9%	−2.3%	35.9%
Apr-99	Jun-00	Late Bull	56.6%	26.2%	55.1%	19.9%	51.8%	16.7%	14.3%	24.2%
Jun-00	Aug-01	Early Bear	50.6%	13.0%	69.7%	14.2%	16.8%	18.3%	23.0%	13.7%
Aug-01	Nov-02	Middle Bear	79.5%	15.3%	37.3%	16.2%	18.9%	17.1%	8.0%	15.0%
Nov-02	Jan-04	Late Bear	2.8%	16.1%	28.9%	11.3%	81.3%	15.6%	49.1%	16.9%
Jan-04	Mar-05	Early Bull	25.8%	14.8%	36.2%	12.2%	68.2%	14.2%	78.2%	13.7%
Mar-05	May-06	Middle Bull	57.6%	21.8%	67.8%	15.6%	53.9%	17.7%	64.0%	20.4%
May-06	Jul-07	Late Bull	123.1%	26.1%	40.8%	15.2%	40.4%	20.8%	31.4%	18.6%
Jul-07	Oct-08	Early Bear	−2.9%	29.9%	3.0%	11.6%	5.8%	22.3%	−11.4%	25.6%
Oct-08	Dec-09	Middle Bear	54.2%	42.3%	32.3%	15.1%	71.9%	30.8%	83.5%	26.4%
Dec-09	Feb-11	Late Bear	33.7%	18.0%	29.7%	10.7%	50.3%	17.2%	56.7%	13.6%
Feb-11	Apr-12	Early Bull	−1.2%	19.8%	29.2%	11.2%	34.3%	15.4%	28.4%	13.8%
Apr-12	Jun-13	Middle Bull	36.6%	18.4%	38.6%	11.8%	9.9%	14.1%	32.3%	14.9%
Jul-13	Sep-14	Late Bull	49.0%	17.0%	43.0%	11.1%	39.5%	16.9%	24.4%	14.1%
Sep-14	Nov-15	Early Bear	44.2%	22.3%	32.0%	12.4%	38.5%	21.3%	36.2%	17.2%
Nov-15	Jan-17	Middle Bear	18.3%	22.6%	1.7%	12.4%	14.1%	22.8%	3.7%	23.9%
			Health Care		Industrials		Technology		Telecommunication
			μ	σ	μ	σ	μ	σ	μ	σ
Dec-96	Feb-98	Early Bull	−67.1%	120.2%	66.7%	19.7%	125.3%	33.4%	61.8%	43.1%
Feb-98	Apr-99	Middle Bull	1742.1%	206.1%	1.4%	25.2%	21.7%	34.2%	36.9%	64.2%
Apr-99	Jun-00	Late Bull	−23.5%	43.2%	1.7%	19.6%	−18.5%	41.0%	193.3%	67.4%
Jun-00	Aug-01	Early Bear	124.2%	41.3%	47.7%	16.2%	0.8%	18.1%	−53.7%	48.3%
Aug-01	Nov-02	Middle Bear	69.1%	71.2%	9.9%	11.9%	23.1%	14.9%	−20.4%	45.0%
Nov-02	Jan-04	Late Bear	61.0%	19.9%	58.6%	13.1%	52.4%	20.2%	182.0%	22.4%
Jan-04	Mar-05	Early Bull	67.6%	28.4%	67.0%	15.1%	66.8%	18.3%	49.1%	23.8%
Mar-05	May-06	Middle Bull	63.6%	20.6%	65.4%	17.6%	118.9%	16.1%	24.2%	41.3%
May-06	Jul-07	Late Bull	26.6%	30.4%	64.6%	16.4%	58.2%	15.8%	70.2%	38.2%
Jul-07	Oct-08	Early Bear	18.7%	38.1%	−1.1%	22.1%	−11.8%	21.8%	−9.0%	26.6%
Oct-08	Dec-09	Middle Bear	52.9%	32.3%	13.5%	23.5%	4.1%	22.1%	−8.9%	33.7%
Dec-09	Feb-11	Late Bear	12.3%	16.9%	24.7%	14.0%	78.3%	16.6%	37.2%	22.9%
Feb-11	Apr-12	Early Bull	67.4%	16.1%	32.8%	14.1%	78.6%	15.7%	38.1%	20.4%
Apr-12	Jun-13	Middle Bull	55.3%	17.7%	33.9%	13.3%	42.3%	12.6%	24.0%	18.2%
Jul-13	Sep-14	Late Bull	35.7%	18.4%	27.4%	13.0%	56.2%	15.8%	158.9%	22.1%
Sep-14	Nov-15	Early Bear	13.7%	21.0%	6.5%	14.6%	2.0%	14.7%	53.8%	22.0%
Nov-15	Jan-17	Middle Bear	31.3%	23.7%	7.7%	17.3%	2.8%	16.1%	54.2%	22.2%

Source: Authors’ calculations.

Figure 7. Daily share price—APN Ltd

Source: Authors’ calculations.

Figure 8. Health-care risk/return performance—excluding Middle Bull 1

Source: Authors’ calculations.

Table 6. Comparing means using t-test ($H_0:{\mu }_1\ge {\mu }_2$, $H_1:{\mu }_1<{\mu }_2$)

Basic materials		Financials		Consumer goods		Consumer services
t statistic	P(H1) true	t statistic	P(H1) true	t statistic	P(H1) true	t statistic	P(H1) true
2.87	2.3%			1.87	6.7%	−0.15	44.4%
0.78	24.1%	1.10	16.7%	0.92	20.6%	1.49	10.6%
		7.55	0.1%	4.50	0.5%	5.10	0.3%
0.98	19.2%	1.81	7.2%	0.72	25.5%	1.86	6.9%
−1.35	12.4%	−0.64	27.9%	−0.55	30.6%	−0.84	22.5%
0.70	26.2%	−4.35	0.6%			−2.28	4.2%
Industrials		Health care		Technology		Telecommunications
0.21	42.1%	0.78	24.0%	−1.29	13.4%	0.58	29.7%
1.03	18.1%	−1.00	18.8%			1.66	8.6%
2.38	3.8%	3.32	1.5%	1.72	8.0%	−1.74	7.8%
1.66	8.6%			3.71	1.0%	1.61	9.1%
−0.08	47.0%	−1.52	10.1%	−0.06	47.6%
−0.71	25.8%	−0.30	39.1%	−2.67	2.8%	−1.11	16.5%

Source: Authors’ calculations.

Figure 9. Probability of sectors outperforming, according to Stovall (1995) theory.

Source: Authors’ calculations.

Stovall, S. (1995). The S&P Guide to sector investing. McGraw-Hill.

 Google Scholar

Hertzberg, M. P., & Beckman, B. A. (1989). Business cycle indicators, revised composite indexes. Business Conditions Digest, 29(1), 97–102. https://doi.org/10.1108/S0573-8555(2010)0000289008

 Google Scholar

Bodie, Z., Kane, A., & Marcus, A. (2012). Essentials of investments (9th ed.). McGraw-Hill.

 Google Scholar

The Conference Board. (2016). The conference board leading economic index for the United States and related composite economic indexes for July 2016. https.//www.conference-board.org/pdf_free/press/US%20LEI%20-%20Tech%20Notes%20Aug%2018%202016.pdf

 Google Scholar

Thorpe, W. A. (2003). Mean variance optimization. Multi-asset portfolio. http.//www.aaii.com/computerizedinvesting/article/mean-variance-optimization-multi-asset-portfolio.pdf

 Google Scholar