新NISAで積立NISAすればとりあえず、ドルコスト平均法の仕組みで、定額投資を20年続ければ資産形成できそうである。
しかし、肝心なこととして、何に投資すべきかは今一つわかりにくい。
また、２０年も待てないよという人向けに、ずばり、株価や企業業績を評価しつつ投資する方法を検証して示してみようと思う。
まず、参考①より、以下の諸量が企業評価に重要だということです。
PER (株価収益率) Price Earnings Ratio
株価が割安か割高かを判断するための指標。利益から見た｢株価の割安性｣。
株価が｢1株当たりの当期純利益（単に1株当たり利益、1株益ともいう）$＝EPS$｣の何倍になっているかを示す指標。
$ PER=\frac {株価}{１株あたり当期純利益} $
参考
PER@金融・証券用語集
PBR (株価純資産倍率) Price Book-value Ratio
株価が 1 株あたり純資産の何倍であるかを示す。株価が直前の本決算期末の｢1株当たり純資産｣の何倍になっているかを示す指標
$ PBR = \frac{株価}{１株あたり純資産} $
純資産と自己資本は、どちらも会社の正味の財産を意味し、貸借対照表（バランスシート）では純資産が自己資本として右側に記載されます。
したがって、以下の式で求まります。
$ PBR = \frac{株価}{１株あたり自己資本} $
$ １株当たり自己資本 = \frac{自己資本}{発行済株式数} $
参考
PBR@金融・証券用語集
ROE(自己資本利益率)Return On Equity
会社が自己資本をどれだけ有効に活用して利益を上げているかを示す指標。
自己資本に対する｢経営の効率性｣を示しています。
ROEが高い水準で推移していれば、その会社の収益性や成長性も有望ですし、株主への利益還元も期待できます。
$ ROE = \frac{当期純利益}{自己資本} $
参考
ROE@金融・証券用語集
EPS（一株益）Earnings Per Share
1株あたりの純利益（当期利益や当期純利益など）を示す指標、
企業が生み出した利益（当期純利益）を発行済みの株式数で割ることで求められる。
$ EPS = \frac{当期純利益}{発行済株式数} $
EPS の数値は投資の判断基準として使用され、EPS が高ければ高いほど株価は上昇しやすくなり、少なくなればなるほど低くなります。また、株価は「株価＝EPS × PER（倍）」の計算式を用いて適正な株価 を求めることもできます。 日経平均の PER は 10～15 倍が妥当とされており、たとえばある銘柄の EPS が 100 円であれば、適正な株価は 1,000～1,500 円と見ることができます。
諸量を求める
次に以下を参考にして、諸量を求めてみようと思う。
やってみると、すぐわかることだが、少し前の記事なのでまんまでは求められないので、少し解説しつつ求める。
lib.py
import pandas as pd
import yfinance as yf
ticker = yf.Ticker("2168.T") #パソナ
hist = ticker.history(period="max")
hist.tail()
Open High Low Close Volume Dividends Stock Splits
Date
2024-04-26 00:00:00+09:00 2230.0 2263.0 2201.0 2260.0 189800 0.0 0.0
2024-04-30 00:00:00+09:00 2270.0 2288.0 2219.0 2221.0 271100 0.0 0.0
2024-05-01 00:00:00+09:00 2216.0 2233.0 2193.0 2232.0 179200 0.0 0.0
2024-05-02 00:00:00+09:00 2245.0 2275.0 2244.0 2266.0 127000 0.0 0.0
2024-05-07 00:00:00+09:00 2285.0 2319.0 2259.0 2264.0 237100 0.0 0.0
financials = ticker.financials
financials
2023-05-31 2022-05-31 2021-05-31 2020-05-31
Tax Effect Of Unusual Items 428620400.0 -81815500.0 -1124060200.0 -823065600.0
Tax Rate For Calcs 0.3614 0.3991 0.3062 0.3062
Normalized EBITDA 21820000000.0 27916000000.0 25123000000.0 15303000000.0
Total Unusual Items 1186000000.0 -205000000.0 -3671000000.0 -2688000000.0
Total Unusual Items Excluding Goodwill 1186000000.0 -205000000.0 -3671000000.0 -2688000000.0
Net Income From Continuing Operation Net Minority Interest 6099000000.0 8621000000.0 6784000000.0 594000000.0
Reconciled Depreciation 5847000000.0 5128000000.0 4456000000.0 4832000000.0
Reconciled Cost Of Revenue 281053000000.0 276424000000.0 251570000000.0 248295000000.0
EBITDA 23006000000.0 27711000000.0 21452000000.0 12615000000.0
EBIT 17159000000.0 22583000000.0 16996000000.0 7783000000.0
Net Interest Income -352000000.0 -265000000.0 -250000000.0 -163000000.0
Interest Expense 393000000.0 293000000.0 290000000.0 200000000.0
Interest Income 41000000.0 28000000.0 40000000.0 37000000.0
Normalized Income 5341620400.0 8744184500.0 9330939800.0 2458934400.0
Net Income From Continuing And Discontinued Operation 6099000000.0 8621000000.0 6784000000.0 594000000.0
Total Expenses 358201000000.0 344012000000.0 314598000000.0 314406000000.0
Total Operating Income As Reported 14377000000.0 22083000000.0 19940000000.0 10577000000.0
Diluted Average Shares 39292617.0 39291737.0 39132377.0 39115590.0
Basic Average Shares 39171484.0 39152550.0 39132377.0 39115590.0
Diluted EPS 155.22 219.41 173.36 15.21
Basic EPS 155.7 220.19 173.36 15.21
Diluted NI Availto Com Stockholders 6099000000.0 8621000000.0 6784000000.0 594000000.0
Net Income Common Stockholders 6099000000.0 8621000000.0 6784000000.0 594000000.0
Otherunder Preferred Stock Dividend 0.0 0.0 0.0 0.0
Net Income 6099000000.0 8621000000.0 6784000000.0 594000000.0
Minority Interests -4608000000.0 -4773000000.0 -2898000000.0 -2728000000.0
Net Income Including Noncontrolling Interests 10707000000.0 13394000000.0 9682000000.0 3323000000.0
Net Income Continuous Operations 10708000000.0 13395000000.0 9682000000.0 3323000000.0
Tax Provision 6058000000.0 8895000000.0 7024000000.0 4260000000.0
Pretax Income 16766000000.0 22290000000.0 16706000000.0 7583000000.0
Other Non Operating Income Expenses 1189000000.0 535000000.0 800000000.0 -156000000.0
Special Income Charges -81000000.0 -184000000.0 -3605000000.0 -2389000000.0
Other Special Charges 81000000.0 52000000.0 367000000.0 169000000.0
Write Off 0.0 132000000.0 3238000000.0 2220000000.0
Net Non Operating Interest Income Expense -352000000.0 -265000000.0 -250000000.0 -163000000.0
Interest Expense Non Operating 393000000.0 293000000.0 290000000.0 200000000.0
Interest Income Non Operating 41000000.0 28000000.0 40000000.0 37000000.0
Operating Income 14377000000.0 22083000000.0 19941000000.0 10578000000.0
Operating Expense 77148000000.0 67588000000.0 63028000000.0 66111000000.0
Gross Profit 91525000000.0 89671000000.0 82969000000.0 76689000000.0
Cost Of Revenue 281053000000.0 276424000000.0 251570000000.0 248295000000.0
Total Revenue 372579000000.0 366096000000.0 334540000000.0 324984000000.0
Operating Revenue 372579000000.0 366096000000.0 334540000000.0 324984000000.0
気になる諸量を書いておくと、以下のとおり
・Diluted EPS（希薄化後1株当たり利益）とは、新株予約権やストックオプションなど、潜在株式の増加を加味して計算されたより厳密な1株当たり当期純利益です
・Net Income 純利益
・EBITDA（イービットディーエー）とは、「Earnings Before Interest Taxes Depreciation and Amortization」の略で、税引前利益に支払利息、減価償却費を加えて算出される利益です
net_income.py
financials.T["Net Income"]
2023-05-31 6099000000.0
2022-05-31 8621000000.0
2021-05-31 6784000000.0
2020-05-31 594000000.0
Name: Net Income, dtype: object
balance_sheet.py
balance_sheet = ticker.balance_sheet
pd.set_option('display.max_rows', None)
balance_sheet
2023-05-31 2022-05-31 2021-05-31 2020-05-31
Treasury Shares Number 2516094.0 2516094.0 2550899.0 2574776.0
Ordinary Shares Number 39174206.0 39174206.0 39139401.0 39115524.0
Share Issued 41690300.0 41690300.0 41690300.0 41690300.0
Total Debt 58332000000.0 53165000000.0 33821000000.0 35923000000.0
Tangible Book Value 27416000000.0 26793000000.0 31796000000.0 24629000000.0
Invested Capital 111023000000.0 102188000000.0 70756000000.0 66090000000.0
Working Capital 49492000000.0 47568000000.0 38087000000.0 36495000000.0
Net Tangible Assets 27416000000.0 26793000000.0 31796000000.0 24629000000.0
Capital Lease Obligations 1307000000.0 965000000.0 1222000000.0 1643000000.0
Common Stock Equity 53998000000.0 49988000000.0 38157000000.0 31810000000.0
Total Capitalization 100727000000.0 92577000000.0 61323000000.0 57450000000.0
Total Equity Gross Minority Interest 71620000000.0 67143000000.0 49776000000.0 42314000000.0
Minority Interest 17622000000.0 17155000000.0 11619000000.0 10504000000.0
Stockholders Equity 53998000000.0 49988000000.0 38157000000.0 31810000000.0
Other Equity Interest 2000000.0 4000000.0 4000000.0 NaN
Treasury Stock 2378000000.0 2378000000.0 2417000000.0 2442000000.0
Retained Earnings 32941000000.0 28238000000.0 20801000000.0 14789000000.0
Additional Paid In Capital 17094000000.0 17786000000.0 14029000000.0 14013000000.0
Capital Stock 5000000000.0 5000000000.0 5000000000.0 5000000000.0
Common Stock 5000000000.0 5000000000.0 5000000000.0 5000000000.0
Total Liabilities Net Minority Interest 203884000000.0 136603000000.0 101865000000.0 98127000000.0
Total Non Current Liabilities Net Minority Interest 59101000000.0 53048000000.0 31090000000.0 33645000000.0
Other Non Current Liabilities 2953000000.0 650000000.0 1051000000.0 910000000.0
Non Current Pension And Other Postretirement Benefit Plans 3575000000.0 3469000000.0 3158000000.0 2793000000.0
Non Current Deferred Taxes Liabilities 2366000000.0 3506000000.0 1028000000.0 1328000000.0
Long Term Debt And Capital Lease Obligation 47653000000.0 43143000000.0 23728000000.0 26643000000.0
Long Term Capital Lease Obligation 924000000.0 554000000.0 562000000.0 1003000000.0
Long Term Debt 46729000000.0 42589000000.0 23166000000.0 25640000000.0
Long Term Provisions 2554000000.0 2280000000.0 2125000000.0 1971000000.0
Current Liabilities 144783000000.0 83555000000.0 70775000000.0 64482000000.0
Other Current Liabilities 79593000000.0 13876000000.0 11560000000.0 9468000000.0
Current Debt And Capital Lease Obligation 10679000000.0 10022000000.0 10093000000.0 9280000000.0
Current Capital Lease Obligation 383000000.0 411000000.0 660000000.0 640000000.0
Current Debt 10296000000.0 9611000000.0 9433000000.0 8640000000.0
Pensionand Other Post Retirement Benefit Plans Current 4693000000.0 4760000000.0 4597000000.0 4125000000.0
Current Provisions 17000000.0 43000000.0 17000000.0 2000000.0
Payables 25304000000.0 30385000000.0 25994000000.0 22475000000.0
Other Payable 10334000000.0 10523000000.0 8359000000.0 6963000000.0
Total Tax Payable 7543000000.0 11127000000.0 11258000000.0 9808000000.0
Accounts Payable 7427000000.0 8735000000.0 6377000000.0 5704000000.0
Total Assets 275504000000.0 203746000000.0 151641000000.0 140441000000.0
Total Non Current Assets 81225000000.0 72618000000.0 42776000000.0 39458000000.0
Other Non Current Assets 1241000000.0 1237000000.0 1393000000.0 1465000000.0
Defined Pension Benefit 2245000000.0 1942000000.0 1348000000.0 1203000000.0
Non Current Prepaid Assets NaN NaN 6751000000.0 5482000000.0
Non Current Deferred Assets 130000000.0 149000000.0 168000000.0 187000000.0
Non Current Deferred Taxes Assets 2909000000.0 2720000000.0 3182000000.0 2757000000.0
Investmentin Financial Assets 6586000000.0 6938000000.0 4966000000.0 4420000000.0
Available For Sale Securities 6586000000.0 6938000000.0 4966000000.0 4420000000.0
Goodwill And Other Intangible Assets 26582000000.0 23195000000.0 6361000000.0 7181000000.0
Other Intangible Assets 19229000000.0 16362000000.0 4717000000.0 5041000000.0
Goodwill 7353000000.0 6833000000.0 1644000000.0 2140000000.0
Net PPE 41498000000.0 36398000000.0 25319000000.0 22087000000.0
Accumulated Depreciation -16179000000.0 -16657000000.0 -14448000000.0 -13134000000.0
Gross PPE 57677000000.0 53055000000.0 39767000000.0 35221000000.0
Construction In Progress 11126000000.0 10668000000.0 9434000000.0 7948000000.0
Other Properties 10415000000.0 11238000000.0 9944000000.0 9726000000.0
Buildings And Improvements 28073000000.0 23841000000.0 17155000000.0 14938000000.0
Land And Improvements 8063000000.0 7308000000.0 3234000000.0 2609000000.0
Properties 0.0 0.0 0.0 0.0
Current Assets 194275000000.0 131123000000.0 108862000000.0 100977000000.0
Other Current Assets 12527000000.0 9561000000.0 6859000000.0 6755000000.0
Inventory 3365000000.0 3560000000.0 2717000000.0 2250000000.0
Other Receivables 11577000000.0 10008000000.0 NaN NaN
Taxes Receivable 2693000000.0 70000000.0 486000000.0 106000000.0
Accounts Receivable 41046000000.0 40973000000.0 44267000000.0 42744000000.0
Gross Accounts Receivable 41046000000.0 40973000000.0 44267000000.0 42744000000.0
Cash Cash Equivalents And Short Term Investments 123067000000.0 66951000000.0 54533000000.0 49122000000.0
Cash And Cash Equivalents 123067000000.0 66951000000.0 54533000000.0 49122000000.0
Share Issued
発行済み株数
Stockholders Equity（株主資本）
総資産から負債総額を差し引いた額で、「自己資本（net worth）」や「純資産（book value）」とも呼ばれます。企業が丸ごと売却された場合、負債をすべて返済した後に株主に残るであろうものを指します。
Share Issued.py
balance_sheet.T["Share Issued"]
2023-05-31 41690300.0
2022-05-31 41690300.0
2021-05-31 41690300.0
2020-05-31 41690300.0
Name: Share Issued, dtype: object
Stockholders Equity.py
balance_sheet.T["Stockholders Equity"]
2023-05-31 53998000000.0
2022-05-31 49988000000.0
2021-05-31 38157000000.0
2020-05-31 31810000000.0
Name: Stockholders Equity, dtype: object
cashflow.py
cashflow = ticker.cashflow
cashflow
2023-05-31 2022-05-31 2021-05-31 2020-05-31
Free Cash Flow -7659000000.0 -6200000000.0 10116000000.0 4720000000.0
Repurchase Of Capital Stock -1506000000.0 0.0 0.0 -5671000000.0
Repayment Of Debt -10925000000.0 -11684000000.0 -9207000000.0 -6485000000.0
Issuance Of Debt 15727000000.0 31629000000.0 7511000000.0 21344000000.0
Issuance Of Capital Stock NaN NaN NaN 762000000.0
Capital Expenditure -13620000000.0 -16315000000.0 -8752000000.0 -6704000000.0
End Cash Position 47919000000.0 56578000000.0 52298000000.0 48147000000.0
Other Cash Adjustment Outside Changein Cash NaN 2000000.0 NaN -117000000.0
Beginning Cash Position 56578000000.0 52298000000.0 48147000000.0 31793000000.0
Effect Of Exchange Rate Changes 174000000.0 244000000.0 95000000.0 -91000000.0
Changes In Cash -8833000000.0 4034000000.0 4056000000.0 16562000000.0
Financing Cash Flow -2292000000.0 23543000000.0 -5147000000.0 12102000000.0
Net Other Financing Charges 42000000.0 7672000000.0 -14000000.0 8656000000.0
Cash Dividends Paid -1392000000.0 -1194000000.0 -756000000.0 -710000000.0
Common Stock Dividend Paid NaN NaN NaN -710000000.0
Net Common Stock Issuance -1506000000.0 0.0 0.0 -4909000000.0
Common Stock Payments -1506000000.0 0.0 0.0 -5671000000.0
Common Stock Issuance NaN NaN NaN 762000000.0
Net Issuance Payments Of Debt 4788000000.0 19884000000.0 -1691000000.0 11859000000.0
Net Short Term Debt Issuance -14000000.0 -61000000.0 5000000.0 -3000000000.0
Net Long Term Debt Issuance 4802000000.0 19945000000.0 -1696000000.0 14859000000.0
Long Term Debt Payments -10925000000.0 -11684000000.0 -9207000000.0 -6485000000.0
Long Term Debt Issuance 15727000000.0 31629000000.0 7511000000.0 21344000000.0
Investing Cash Flow -12502000000.0 -29624000000.0 -9665000000.0 -6964000000.0
Net Other Investing Changes 1082000000.0 -2226000000.0 -702000000.0 -932000000.0
Net Investment Purchase And Sale 1195000000.0 -649000000.0 -26000000.0 -201000000.0
Sale Of Investment 1790000000.0 95000000.0 7000000.0 41000000.0
Purchase Of Investment -595000000.0 -744000000.0 -33000000.0 -242000000.0
Net Business Purchase And Sale -1268000000.0 -10451000000.0 -197000000.0 0.0
Purchase Of Business -1268000000.0 -10451000000.0 -197000000.0 0.0
Net Intangibles Purchase And Sale -4591000000.0 -4683000000.0 -1721000000.0 -1894000000.0
Purchase Of Intangibles -4591000000.0 -4683000000.0 -1721000000.0 -1894000000.0
Net PPE Purchase And Sale -8930000000.0 -11619000000.0 -7021000000.0 -3955000000.0
Sale Of PPE 99000000.0 13000000.0 10000000.0 855000000.0
Purchase Of PPE -9029000000.0 -11632000000.0 -7031000000.0 -4810000000.0
Operating Cash Flow 5961000000.0 10115000000.0 18868000000.0 11424000000.0
Taxes Refund Paid -12932000000.0 -8084000000.0 -7820000000.0 -5406000000.0
Interest Received Cfo 98000000.0 86000000.0 87000000.0 79000000.0
Interest Paid Cfo -374000000.0 -286000000.0 -292000000.0 -197000000.0
Change In Working Capital -2253000000.0 -9431000000.0 1625000000.0 1238000000.0
Change In Other Current Liabilities 816000000.0 1154000000.0 2282000000.0 804000000.0
Change In Other Current Assets -3143000000.0 -2536000000.0 798000000.0 -230000000.0
Change In Payable 39000000.0 -862000000.0 527000000.0 2803000000.0
Change In Prepaid Assets 1061000000.0 -1201000000.0 -886000000.0 1389000000.0
Change In Inventory 199000000.0 -315000000.0 -458000000.0 -216000000.0
Change In Receivables -1188000000.0 -6112000000.0 -1422000000.0 -3536000000.0
Other Non Cash Items -835000000.0 -394000000.0 -317000000.0 534000000.0
Depreciation And Amortization 5847000000.0 5128000000.0 4456000000.0 4832000000.0
Amortization Cash Flow 721000000.0 709000000.0 662000000.0 819000000.0
Depreciation 5126000000.0 4419000000.0 3794000000.0 4013000000.0
Gain Loss On Investment Securities -1170000000.0 97000000.0 435000000.0 472000000.0
Net Income From Continuing Operations 16766000000.0 22290000000.0 16706000000.0 7583000000.0
EPS（1株当たり当期純利益）Earnings Per Shareを求める
$ EPS = \frac{当期純利益}{発行済株式数} $
eps = financials.T["Net Income"] / balance_sheet.T["Share Issued"]
eps
2023-05-31 146.293023
2022-05-31 206.786711
2021-05-31 162.723703
2020-05-31 14.247919
dtype: object
ROE(自己資本利益率)Return On Equityを求める
$ ROE = \frac{当期純利益}{自己資本} $
roe = financials.T["Net Income"] / balance_sheet.T["Stockholders Equity"]
roe
2023-05-31 0.112949
2022-05-31 0.172461
2021-05-31 0.177792
2020-05-31 0.018673
dtype: object
PER (株価収益率) Price Earnings Ratioを求める
$ PER=\frac {株価}{１株あたり当期純利益} $
per = hist["Close"]["2023-05-31"] / eps["2023-05-31"]
per
11.88026584686014
per = hist["Close"]["2023-05-20":"2023-06-10"] / eps["2023-05-31"]
per
Date
2023-05-22 00:00:00+09:00 12.461106
2023-05-23 00:00:00+09:00 12.246606
2023-05-24 00:00:00+09:00 11.985185
2023-05-25 00:00:00+09:00 11.958372
2023-05-26 00:00:00+09:00 11.770684
2023-05-29 00:00:00+09:00 12.105841
2023-05-30 00:00:00+09:00 12.023813
2023-05-31 00:00:00+09:00 11.880266
2023-06-01 00:00:00+09:00 11.716212
2023-06-02 00:00:00+09:00 11.941786
2023-06-05 00:00:00+09:00 12.071663
2023-06-06 00:00:00+09:00 12.003307
2023-06-07 00:00:00+09:00 11.955457
2023-06-08 00:00:00+09:00 11.661527
2023-06-09 00:00:00+09:00 11.647856
Name: Close, dtype: float64
PBR (株価純資産倍率) Price Book-value Ratioを求める
\begin{align}
PBR &= \frac{株価}{１株当たり純資産} \\
&= \frac{株価}{１株当たり自己資本} \\
&= \frac{株価＊発行済株式数}{自己資本}
\end{align}
１株当たり自己資本 = \frac{自己資本}{発行済株式数}
したがって、以下の式が成り立つ、
PBR = ROE * PER
pbr.py
pbr = roe['2023-05-31'] * per
pbr
Date
2023-05-22 00:00:00+09:00 1.407465
2023-05-23 00:00:00+09:00 1.383237
2023-05-24 00:00:00+09:00 1.353710
2023-05-25 00:00:00+09:00 1.350682
2023-05-26 00:00:00+09:00 1.329483
2023-05-29 00:00:00+09:00 1.367338
2023-05-30 00:00:00+09:00 1.358073
2023-05-31 00:00:00+09:00 1.341860
2023-06-01 00:00:00+09:00 1.323330
2023-06-02 00:00:00+09:00 1.348808
2023-06-05 00:00:00+09:00 1.363478
2023-06-06 00:00:00+09:00 1.355757
2023-06-07 00:00:00+09:00 1.350353
2023-06-08 00:00:00+09:00 1.317153
2023-06-09 00:00:00+09:00 1.315609
Name: Close, dtype: float64
PER（会社予想）用語(連)0.88倍(15:00)
PBR（実績）用語(連)1.71倍(15:00)
EPS（会社予想）用語(連)2,552.55(2024/05)
BPS（実績）用語(連)1,315.48(2023/05)
ROE（実績）用語(連)11.73%(2023/05)
(株)パソナグループ【2168.T】@yahoo finace
投資判断; PBR vs PER
per_pbr.py
per = hist["Close"]["2023-05-20":"2024-05-09"] / eps["2023-05-31"]
pbr = roe['2023-05-31'] * per #グラフの傾きはroe['2023-05-31']
plot.py
x_avg = 14
y_avg = 1
y = np.linspace(0,30,100)
y_ = np.linspace(1,1,100)
x = np.linspace(0.5,2.5,100)
#x_ = np.linspace(14,14,100)
x_ = np.linspace(x_avg,x_avg,100)
x_1 = np.linspace(np.average(per),np.average(per),238)
x_2 = np.linspace(np.average(pbr),np.average(pbr),238)
fig, (ax1,ax2,ax3) = plt.subplots(3,1,figsize=(1.6180 * 4, 4*2))
ax3.plot(per)
ax3.plot(per.index,x_1)
ax3.set_xlabel("period")
ax3.set_ylabel('per')
ax2.plot(pbr)
ax2.plot(pbr.index,x_2)
ax2.set_xlabel("period")
ax2.set_ylabel('pbr')
ax1.plot(per, pbr, 'o', label = 'パソナG')
ax1.set_xlabel("per")
ax1.set_ylabel('pbr')
ax1.plot(x_, x)
ax1.plot(y,y_)
ax1.plot(x_avg, y_avg, 'o')
ax1.set_ylim(0.5,2.5)
ax1.set_xlim(0,30)
ax1.legend()
複数銘柄のグラフ
コード改善
stock_dic.py
stock_dict = {'JT':'2914.T', #'2914.JP',
'セガサミ':'6460.T',
'NTT':'9432.T', #'9432.JP',
'トヨタ':'7203.T', # '7203.JP',
'三菱自動車':'7211.T', #'7211.JP',
'日本航空':'9201.T', #'9201.JP',
'全日空':'9202.T',
'ENEOS':'5020.T', #'5020.JP',
}
plot.py
def plot_pbrper(pbr, per, name):
x_avg = 14
y_avg = 1
y = np.linspace(0,30,100)
y_ = np.linspace(1,1,100)
x = np.linspace(0.,2.5,100)
#x_ = np.linspace(14,14,100)
x_ = np.linspace(x_avg,x_avg,100)
x_1 = np.linspace(np.average(per),np.average(per),279) #279;期間日数
x_2 = np.linspace(np.average(pbr),np.average(pbr),279)
fig, (ax1,ax2,ax3) = plt.subplots(3,1,figsize=(1.6180 * 4, 4*2))
ax3.plot(per)
ax3.plot(per.index,x_1)
ax3.set_xlabel("period")
ax3.set_ylabel('per')
ax2.plot(pbr)
ax2.plot(pbr.index,x_2)
ax2.set_xlabel("period")
ax2.set_ylabel('pbr')
ax1.plot(per, pbr, 'o', label = name)
ax1.set_xlabel("per")
ax1.set_ylabel('pbr')
ax1.plot(x_, x)
ax1.plot(y,y_)
ax1.plot(x_avg, y_avg, 'o')
#ax1.set_ylim(0.,2.5)
#ax1.set_xlim(0,30)
ax1.legend()
calc.py
for name, stock in stock_dict.items():
ticker = yf.Ticker(stock) #'日産自動車':'7201.T', パソナ: 2168.T 京急: 9006.T
hist = ticker.history(period="max")
financials = ticker.financials
balance_sheet = ticker.balance_sheet
eps = financials.T["Net Income"] / balance_sheet.T["Share Issued"]
roe = financials.T["Net Income"] / balance_sheet.T["Stockholders Equity"]
per = hist["Close"]["2023-03-20":"2024-05-09"] / eps[financials.columns[0]] #基準日"2023-03-31"がまちまち
pbr = roe[financials.columns[0]] * per #グラフの傾きはroe[financials.columns[0]]
plot_pbrper(pbr,per, name)
目標株価
参考
個別銘柄レポート/理論株価
利用する基本的な式は以下のものを利用する。
$株価 ＝ EPS * PER$
ここで、参考では以下の通りで求めている。
引用.
PER基準
最新の予想EPS × 過去75日間のPERレンジ（±2σ）
理論株価：過去75日間のPER平均値をあてはめた場合の株価水準
上値目途：PERの上限（平均+2σ）まで買われた場合の株価水準
下値目途：PERの下限（平均-2σ）まで売られた場合の株価水準
$株価 ＝ BPS * PBR$
引用.
PBR基準
最新の実績BPS × 過去75日間のPBRレンジ（±2σ）
理論株価：過去75日間のPBR平均値をあてはめた場合の株価水準
上値目途：PBRの上限（平均+2σ）まで買われた場合の株価水準
下値目途：PBRの下限（平均-2σ）まで売られた場合の株価水準
目標株価（混乱を避けるためにこちらは目標株価とする）を上記を参考を参照して求めてみようと思う
ただし、目標株価は、pbr及びperの年間(2023.01-2024.5.09)の平均から求められるそれぞれの目標株価とし、
上値と下値は、年間(2023.01-2024.05.09)の$±２σ$から求められるものとする。
PER PBRの平均と±2σをみる
target.py
x_avg = 14
y_avg = 1
y = np.linspace(0,30,100)
y_ = np.linspace(1,1,100)
x = np.linspace(0.5,2.5,100)
#x_ = np.linspace(14,14,100)
x_ = np.linspace(x_avg,x_avg,100)
x_1 = np.linspace(np.average(per),np.average(per),238)
x_2 = np.linspace(np.average(pbr),np.average(pbr),238)
per_mean = np.linspace(statistics.mean(per), statistics.mean(per), len(x_1), dtype=float)
per_var = per_mean + 2*np.linspace(np.sqrt(statistics.variance(per)), np.sqrt(statistics.variance(per)), len(x_1), dtype=float)
per_var_ = per_mean - 2*np.linspace(np.sqrt(statistics.variance(per)), np.sqrt(statistics.variance(per)), len(x_1), dtype=float)
pbr_mean = np.linspace(statistics.mean(pbr), statistics.mean(pbr), len(x_2), dtype=float)
pbr_var = pbr_mean + 2*np.linspace(np.sqrt(statistics.variance(pbr)), np.sqrt(statistics.variance(pbr)), len(x_2), dtype=float)
pbr_var_ = pbr_mean - 2*np.linspace(np.sqrt(statistics.variance(pbr)), np.sqrt(statistics.variance(pbr)), len(x_2), dtype=float)
fig, (ax1,ax2,ax3) = plt.subplots(3,1,figsize=(1.6180 * 4, 4*2))
ax3.plot(per)
ax3.plot(per.index,x_1)
ax3.plot(per.index,per_var)
ax3.plot(per.index,per_var_)
ax3.set_xlabel("period")
ax3.set_ylabel('per')
ax2.plot(pbr)
ax2.plot(pbr.index,x_2)
ax2.plot(pbr.index,pbr_var)
ax2.plot(pbr.index,pbr_var_)
ax2.set_xlabel("period")
ax2.set_ylabel('pbr')
ax1.plot(per, pbr, 'o', label = 'パソナG')
ax1.set_xlabel("per")
ax1.set_ylabel('pbr')
ax1.plot(x_, x)
ax1.plot(y,y_)
ax1.plot(x_avg, y_avg, 'o')
ax1.set_ylim(0.5,2.5)
ax1.set_xlim(0,30)
ax1.legend()
目標株価と上値、下値を求める
stock.py
stock_dict = {'JT':'2914.T', #'2914.JP',
'セガサミ':'6460.T',
'NTT':'9432.T', #'9432.JP',
'トヨタ':'7203.T', # '7203.JP',
'三菱自動車':'7211.T', #'7211.JP',
'日本航空':'9201.T', #'9201.JP',
'全日空':'9202.T',
'ENEOS':'5020.T', #'5020.JP',
'川崎汽':'9107.T', #'9107.JP',
'郵船':'9101.T', #'9101.JP'
'商船三井':'9104.T', #'9104.JP'
'郵政':'6178.T', #'6178.JP'
'ゆうちょ銀':'7182.T', #'7182.JP'
'日本製鉄':'5401.T', #'5401.JP'
}
plot_pbrper.py
def plot_pbrper(pbr, per, name):
x_avg = 14
y_avg = 1
y = np.linspace(0,30,100)
y_ = np.linspace(1,1,100)
x = np.linspace(0.,2.5,100)
#x_ = np.linspace(14,14,100)
x_ = np.linspace(x_avg,x_avg,100)
x_1 = np.linspace(np.average(per),np.average(per),279)
x_2 = np.linspace(np.average(pbr),np.average(pbr),279)
x_3 = np.linspace(0, 0, len(x_1), dtype=float)
per_mean, per_var, per_var_, pbr_mean, pbr_var, pbr_var_ = x_3,x_3,x_3,x_3,x_3,x_3
try:
per_mean = np.linspace(statistics.mean(per), statistics.mean(per), len(x_1), dtype=float)
per_var = per_mean + 2*np.linspace(np.sqrt(statistics.variance(per)), np.sqrt(statistics.variance(per)), len(x_1), dtype=float)
per_var_ = per_mean - 2*np.linspace(np.sqrt(statistics.variance(per)), np.sqrt(statistics.variance(per)), len(x_1), dtype=float)
pbr_mean = np.linspace(statistics.mean(pbr), statistics.mean(pbr), len(x_2), dtype=float)
pbr_var = pbr_mean + 2*np.linspace(np.sqrt(statistics.variance(pbr)), np.sqrt(statistics.variance(pbr)), len(x_2), dtype=float)
pbr_var_ = pbr_mean - 2*np.linspace(np.sqrt(statistics.variance(pbr)), np.sqrt(statistics.variance(pbr)), len(x_2), dtype=float)
'''
fig, (ax1,ax2,ax3) = plt.subplots(3,1,figsize=(1.6180 * 4, 4*2))
ax3.plot(per)
ax3.plot(per.index,x_1)
ax3.plot(per.index,per_var)
ax3.plot(per.index,per_var_)
ax3.set_xlabel("period")
ax3.set_ylabel('per')
ax2.plot(pbr)
ax2.plot(pbr.index,x_2)
ax2.plot(pbr.index,pbr_var)
ax2.plot(pbr.index,pbr_var_)
ax2.set_xlabel("period")
ax2.set_ylabel('pbr')
ax1.plot(per, pbr, 'o', label = name)
ax1.set_xlabel("per")
ax1.set_ylabel('pbr')
ax1.plot(x_, x)
ax1.plot(y,y_)
ax1.plot(x_avg, y_avg, 'o')
#ax1.set_ylim(0.5,2.5)
#ax1.set_xlim(0,30)
ax1.legend()
'''
except:
pass
return per_mean, per_var, per_var_, pbr_mean, pbr_var, pbr_var_
目標株価.py
ideal_close_per = []
# ideal_close_pbr = []
ideal_close_per_var = []
ideal_close_per_var_ = []
per_i = []
pbr_i = []
roe_i = []
eps_i = []
name_i =[]
close_i = []
df_ideal = pd.DataFrame()
for name, stock in stock_dict.items():
print(name)
ticker = yf.Ticker(stock)
hist = ticker.history(period="max")
financials = ticker.financials
balance_sheet = ticker.balance_sheet
eps = financials.T["Net Income"] / balance_sheet.T["Share Issued"]
roe = financials.T["Net Income"] / balance_sheet.T["Stockholders Equity"]
per = hist["Close"]["2023-03-20":"2024-05-09"] / eps[financials.columns[0]]
pbr = roe[financials.columns[0]] * per
per_mean, per_var, per_var_, pbr_mean, pbr_var, pbr_var_ = plot_pbrper(pbr,per, name)
ideal_close_per.append(per_mean[0]*eps[financials.columns[0]])
ideal_close_per_var.append(per_var[0]*eps[financials.columns[0]])
ideal_close_per_var_.append(per_var_[0]*eps[financials.columns[0]])
per_i.append(per_mean[0])
pbr_i.append(pbr_mean[0])
roe_i.append(pbr_mean[0]/per_mean[0])
eps_i.append(eps[financials.columns[0]])
close_i.append(hist["Close"]["2024-05-09"])
name_i.append(name)
df_ideal['close'] = close_i
df_ideal['ideal_close_per'] = ideal_close_per
# df_ideal['ideal_close_pbr'] = ideal_close_pbr
df_ideal['ideal_close_per_var'] = ideal_close_per_var
df_ideal['ideal_close_per_var_'] = ideal_close_per_var_
df_ideal['per'] = per_i
df_ideal['pbr'] = pbr_i
df_ideal['roe'] = roe_i
df_ideal['eps'] = eps_i
df_ideal.index = name_i
df_ideal_ = df_ideal.dropna()
df_ideal_.rename(columns={'close':'最新株価',
'ideal_close_per': '目標株価',
'ideal_close_per_var':'上値',
'ideal_close_per_var_':'下値',
}, inplace= True)
df_ideal_order = df_ideal_.sort_values(['目標株価'], ascending=False)
#df_ideal_order
eps_mean = np.mean(df_ideal_order['eps'])
roe_mean = np.mean(df_ideal_order['roe'])
per_mean = np.mean(df_ideal_order['per'])
pbr_mean = np.mean(df_ideal_order['pbr'])
目標株価_mean = np.mean(df_ideal_order['目標株価'])
最新株価_mean = np.mean(df_ideal_order['最新株価'])
fig, (ax1,ax2, ax3) = plt.subplots(3,1,figsize=(1.6180 * 4, 4*3))
ax1.plot(df_ideal_order['eps'], df_ideal_order['roe'], 'o')
ax1.plot(eps_mean, roe_mean, 'o', label = 'center')
ax1.set_xlabel("eps")
ax1.set_ylabel('roe')
ax1.legend()
ax2.plot(df_ideal_order['per'], df_ideal_order['pbr'], 'o')
ax2.plot(per_mean, pbr_mean, 'o', label = 'center')
ax2.set_xlabel("per")
ax2.set_ylabel('pbr')
ax2.legend()
ax3.scatter(df_ideal_order['目標株価'], df_ideal_order['上値'], s=10, c="k", marker='o') #, s=25)
ax3.scatter(df_ideal_order['目標株価'], df_ideal_order['下値'], s=10, c="k", marker= 'o') #, s=25)
ax3.scatter(df_ideal_order['目標株価'], df_ideal_order['最新株価'], s=50, c="b", marker= 'o') #, s=50)
ax3.scatter(目標株価_mean, 最新株価_mean, s=100, c="r" , marker='o', label = 'center') #, s=100)
ax3.set_xlabel("目標株価")
ax3.set_ylabel('最新株価')
ax3.legend()
df_ideal_order
目標株価でソート
df_ideal_order.py
最新株価 目標株価 上値 下値 per pbr roe eps
安川電機 6503.000000 5809.010971 6676.878353 4941.143590 29.917309 4.458165 0.149016 194.168898
TOWA 10940.00000 5251.862234 10965.01118 -461.286715 17.895320 2.784129 0.155579 293.476857
AGC 5448.000000 5139.014255 5730.066032 4547.962477 16.982278 0.772176 0.045469 302.610419
JFE 5448.000000 5139.014255 5730.066032 4547.962477 16.982278 0.772176 0.045469 302.610419
住友精化 5050.000000 4558.585176 5323.584655 3793.585697 7.413521 0.735022 0.099146 614.901485
東エレデバ 4975.000000 4355.308915 7286.283021 1424.334809 15.547977 3.606102 0.231934 280.120626
KDDI 4283.000000 4348.760318 4811.416941 3886.103695 14.781405 1.954929 0.132256 294.204794
武田薬品 4070.000000 4232.588129 4533.791097 3931.385160 21.125704 1.053994 0.049892 200.352523
太陽誘電 3321.000000 3818.398336 4431.167244 3205.629428 21.417386 1.561267 0.072897 178.284986
キヤノン 4338.000000 3675.004369 4582.806637 2767.202101 20.091681 1.574501 0.078366 182.911743
JT 4342.000000 3384.973354 4343.387159 2426.559548 14.037116 1.767538 0.125919 241.144500
日本製鉄 3410.000000 3193.874283 3794.854187 2592.894380 4.373396 0.725926 0.165987 730.296086
住友商事 4159.000000 3021.430688 3900.439105 2142.422270 6.690879 1.000534 0.149537 451.574548
日本航空 2711.500000 2778.897753 3087.286762 2470.508745 35.289693 1.488172 0.042170 78.745309
三井住友ト 3216.000000 2711.482387 3309.767845 2113.196929 10.453331 0.714847 0.068385 259.389323
オリックス 3426.000000 2669.875546 3318.730417 2021.020676 12.073218 0.982217 0.081355 221.140337
トヨタ 3528.000000 2658.649015 3866.833686 1450.464345 17.694899 1.530621 0.086501 150.249457
野村マイクロ 5170.000000 2482.992727 5415.064539 -449.079085 17.364354 4.711409 0.271326 142.993671
三菱マ 3019.000000 2467.795876 2968.404677 1967.187074 15.961108 0.546903 0.034265 154.613065
セガサミ 2084.500000 2399.555217 3195.384511 1603.725923 12.600537 1.747072 0.138651 190.432781
ルネサス 2503.500000 2367.999826 2922.843305 1813.156348 13.757969 2.317010 0.168412 172.118414
鹿島建設 2990.500000 2342.343251 3174.272141 1510.414362 11.077063 1.176604 0.106220 211.458865
丸紅 2931.000000 2275.540891 2800.571365 1750.510417 7.117424 1.342984 0.188690 319.714107
NTTD 2397.500000 2014.921960 2461.570482 1568.273438 18.844294 1.945740 0.103254 106.924777
LIXIL 1793.000000 1797.614175 2027.937213 1567.291137 32.275179 0.825208 0.025568 55.696489
ソフトバンク 1883.500000 1664.662140 2034.728624 1294.595657 14.997157 3.581652 0.238822 110.998514
本田技研 1748.000000 1508.267334 1931.618785 1084.915884 12.582367 0.732848 0.058244 119.871513
世紀東急 1625.000000 1494.567121 2128.248022 860.886219 49.630379 1.410324 0.028417 30.113957
ゆうちょ銀 1571.500000 1287.406892 1691.642615 883.171170 14.613956 0.494073 0.033808 88.094344
郵政 1506.000000 1207.557278 1569.654378 845.460177 10.246691 0.438299 0.042775 117.848508
三菱ＵＦＪ 1583.500000 1187.621313 1652.548645 722.693982 13.495969 0.874467 0.064795 87.998222
東洋建設 1286.000000 1117.674607 1396.302279 839.046935 18.648563 1.468681 0.078756 59.933550
アルヒ 850.000000 899.970664 1047.364783 752.576545 11.514710 0.991042 0.086067 78.158345
住石HD 1237.000000 862.985282 2382.691848 -656.721284 13.859740 2.496128 0.180099 62.265620
ユ海運 848.000000 834.813117 1028.877653 640.748581 8.150281 1.267105 0.155468 102.427530
日産自 576.500000 563.817818 659.737819 467.897816 10.724265 0.463437 0.043214 52.574029
ENEOS 706.099976 556.250332 728.844971 383.655693 11.734514 0.589903 0.050271 47.402928
NTT 167.899994 168.105768 186.059901 150.151636 12.547877 1.777994 0.141697 13.397148
最新株価vs目標株価
株価変動
plot_stock.py
def plot_stockstock(name, close, ideal_sktock, ideal_up, ideal_down):
close_ = pd.DataFrame()
close_ = close["2023-03-20":"2024-05-09"]
x_1 = np.linspace(np.average(ideal_sktock),np.average(ideal_sktock),len(close_))
x_2 = np.linspace(np.average(ideal_up),np.average(ideal_up),len(close_))
x_3 = np.linspace(np.average(ideal_down),np.average(ideal_down),len(close_))
fig, (ax1) = plt.subplots(1,1,figsize=(1.6180 * 4, 4*1))
ax1.plot(close_.index, close_, 'o', label = name)
ax1.plot(close_.index, x_1, 'o', label = name + '_平均')
ax1.plot(close_.index, x_2, 'o', label = name + '_+2σ')
ax1.plot(close_.index, x_3, 'o', label = name + '_-2σ')
ax1.set_xlabel("period")
ax1.set_ylabel('stock')
ax1.legend()
call_stock.py
...
for name, stock in stock_dict.items():
...
plot_stockstock(name, hist["Close"], per_mean[0]*eps[financials.columns[0]], per_var[0]*eps[financials.columns[0]], per_var_[0]*eps[financials.columns[0]])
まとめ
・目標株価を計算してみた。割と±２σが良いリトレースメントになっている
・perベースの目標株価を求めてみた
・株価vs目標株価のグラフは直線的である
・roe vs epsやpbr vs perは、購入時の参考になりそう
・そもそも目標株価の求め方を精査する余地がある
・配当や設備投資を目標株価の算定に反映したい